LinkedIn X-Ray Search for Finding B2B Buyers on Google

LinkedIn X-ray search is a Google search that returns only LinkedIn profile pages. You enter a query such as site:linkedin.com/in ("head of sales" OR "sales director") "software development" -assistant into Google.

For a B2B team, X-ray search does one job well. In ten minutes and for free, it shows whether the buyers you picture really exist, under the titles you expect, in the places you want to sell. What it cannot do is turn those names into a complete, verified list you can refill every month.

So my rule fits on one line: X-ray to scout, pay to build. Use Google to test titles, cities and niches. Once a test shows the market is there and you need thousands of people from it, move to a paid sourcing tool and add a verification step.

What can X-ray search find for a B2B team, and what can it not?

The whole method rests on one operator. According to Google Search Central, site: limits what comes back to a single domain, a URL or the start of a URL path. Point it at linkedin.com/in and only personal profile pages come back. Add titles, industries and places, and Google filters those pages by the words on them.

The name comes from looking into the platform from outside. You do not log in to LinkedIn to run the search, and you do not need a paid seat to read the results.

What a B2B team can get from it:

• names and profile links of people whose public profile sits in Google's index
• the title and company wording on each result, which shows how a niche names its buyers
• companies you had never heard of in a city or vertical you thought you knew
• a fast yes or no on whether a new market deserves a proper list

What it cannot give you:

• A complete list. The same Google page says a site: query does not necessarily return every indexed URL under the prefix. It also says a plain site: query does not rank results, so do not read the order as a ranking.
• Structured filters. Google has no field for headcount, seniority or industry. It only matches words.
• Contact data. A profile found on Google gives you a name and a link. Email addresses have to come from a data tool, then get verified.
• Any record keeping. You get no export, no refresh, and no way to see who on your team already has the person.

X-ray is a scouting trip, nothing more. Treat the search results as your list and you are sending messages to guesses.

Where does X-ray search fit in how my team builds lists?

When a list underperforms, I check where the names came from before I read a single message, because copy cannot fix a bad list. My team builds client lists in paid tools, among them Apollo, ZoomInfo, Sales Navigator, RocketReach and UpLead, because those lists beat scraped ones.

A campaign needs a current title, an email address that does not bounce, and a record of who already owns the contact. A page of Google results gives you none of that. Names to check, that's it.

So X-ray earns its place at the very start, in three spots:

1. Finding lookalikes. I begin with a client's existing and former customers and look for companies built like them. A quick Google string with one customer title and a city shows whether those lookalikes exist.
2. Testing titles. I target the titles a niche really uses for the person who decides, and a bare "manager" never makes it onto that list. A generic list rarely carries the title of whoever signed the last deal. So I go through the client's won deals, note who actually signed, and put that title in the string. Google shows you those titles in real profiles before you commit a paid search to them.
3. Spot-checking a bought list. Pull 20 names from any file and check each profile manually. When you cannot explain why that person would care, the file is not ready.

The real build happens elsewhere. Every address goes through NeverBounce or DeBounce before a send, and we keep bounces below 0.5%. I want the first list to hold at least 20,000 contacts.

Who pays for what? In my agency's B2B lead generation model, list building is covered by the monthly fee, along with strategy, copy, campaign setup, sending, reply handling and reporting. The client pays the data tools, mailboxes, domains and other software directly, because the right stack changes with the market, the volume and the size of the sales team.

For the full build, from criteria to the final file, here is how to build a B2B lead list.

Which Google operators matter for LinkedIn X-ray search?

Five operators do almost all the work. Google's search help covers four of them: site:, the minus sign for leaving words out, quotes for exact phrases and filetype: (Google Search Help). The fifth, OR in capitals, matches either of two terms. Google's operator overview defines filetype: by content-type header or file extension (Google Search Central).

Rules for combining them:

• Start every string with site:linkedin.com/in, with no space after the colon.
• Put quotes around every title longer than one word.
• Write OR in capitals, and wrap the alternatives in brackets so they read as one group.
• Give each excluded word its own minus sign, with no space between the sign and the word.
• Change one part at a time, so that when a string returns rubbish you know which part caused it.
• Save every string that works in a shared doc, with the date, the market and what it found.

Many guides also push intitle: and inurl:. Google's operator overview does not list them, so keep them as an occasional extra.

Which X-ray strings should a B2B team copy?

Recruiter guides fill this section with developers who are open to work. A seller needs a different list: people who sign, buy or own a budget. The five strings below start from buyer titles. Swap in your own industry and place.

Operations leads at construction and civil engineering firms:

site:linkedin.com/in ("operations director" OR "head of operations" OR "COO") ("construction" OR "civil engineering") "Texas" -assistant -associate -intern

Founders and owners of agencies:

site:linkedin.com/in ("founder" OR "co-founder" OR "owner") ("marketing agency" OR "advertising agency") "London" -assistant -associate -intern

Sales leaders at software development companies:

site:linkedin.com/in ("head of sales" OR "VP of sales" OR "sales director") ("software development" OR "custom software") -assistant -associate -recruiter

Partnership leads at SaaS companies:

site:linkedin.com/in ("head of partnerships" OR "partnerships director" OR "VP partnerships") "SaaS" -assistant -associate -intern

Demand generation leads:

site:linkedin.com/in ("head of demand generation" OR "demand generation director" OR "VP demand generation") "B2B" -assistant -associate

Why is "manager" missing from the exclusions?

I cut generic titles like Assistant, Manager and Associate from every target list.

On Google, though, the minus sign drops any result that contains the word anywhere on the page. A founder whose profile mentions an old "account manager" job would vanish along with the real noise.

So the strings cut Assistant, Associate and Intern (Recruiter in the sales-leader string), which are safer to drop at the search stage. Manager titles come out later, when you review the titles in the list itself.

Before anything is sent, audit the lead list.

Worked example: one city, one buyer

Take a hypothetical digital agency in Manchester that wants owners of construction firms nearby. I prefer a local audience whenever the offer allows, since it is easier to start a conversation when the prospect knows you live in the same city.

site:linkedin.com/in ("owner" OR "founder" OR "managing director") ("construction" OR "building contractor") ("Manchester" OR "Salford" OR "Stockport") -assistant -associate -intern

What to do with the results:

1. Open the first 20 profiles. Write down each person's title, company and town.
2. For each one, ask: could this person agree to a call without checking with anyone?
3. If most pass, the titles look right. If few pass, change the title group and run the string again. Set your pass mark before you open the first profile, so the result cannot bend to what you hoped to see.
4. Compare the companies with the agency's best current clients. If they match in size and type, the lookalike logic holds.
5. Move the confirmed titles and towns into a paid tool. Build the full list there and verify it.

Worked example: one country, two reps

Now a US software development company with two reps chasing sales leaders. My rule is that each rep owns their own accounts, so no buyer ever hears from two people at the same seller.

Rep A:

site:linkedin.com/in ("head of sales" OR "VP of sales" OR "sales director") ("software development" OR "custom software") ("Texas" OR "Florida") -assistant -associate -recruiter

Rep B uses the same string with ("New York" OR "Illinois") as the place group.

The catch is that a place name can match anywhere on the page. A sales director who once worked in Dallas, Texas, and now lives in Chicago, Illinois, can show up in both searches. The query cannot enforce territory, so the list has to. Merge both reps' results, remove duplicate companies, and give every company to exactly one rep before anyone sends.

Is LinkedIn X-ray search legal?

Typing a query into Google and reading the public pages it returns is the same act as any other Google search. The legal questions start with two other steps: collecting profiles with software, and what you do with the personal data afterwards.

Automated collection is a separate fight. The best-known US case is hiQ Labs v. LinkedIn, decided by the Ninth Circuit in April 2022. It concerned a company that used automated bots to scrape public LinkedIn profiles and sold analytics built on that data (Ninth Circuit opinion).

The court said the case dealt only with profiles visible to the general public. The opinion also records that LinkedIn's robots.txt file prohibited automated bots from accessing its servers, apart from some express permissions.

The court upheld a preliminary injunction for hiQ. hiQ had raised serious questions about how the federal anti-hacking law, the Computer Fraud and Abuse Act, applies here. That ruling came at an early stage of a dispute between two companies. It is no green light for bots, the dispute continued after this ruling, and it says nothing about privacy law or LinkedIn's own terms.

Privacy and marketing rules govern the next step. In the UK, the ICO's direct marketing guidance explains how to send electronic marketing by phone, email or text lawfully. It has a dedicated item on business-to-business marketing and another on using data broking services (ICO).

The ICO's legitimate interests guidance has separate sections on direct marketing and on business-to-business contacts. It was last updated on 23 March 2026 to reflect the Data (Use and Access) Act (ICO legitimate interests guidance). Read both sections before you email anyone you found through a search.

What this means in practice:

• Keep X-ray search manual. If you need volume, get it from a licensed data tool.
• Record where each contact came from and why they fit your offer.
• Keep an exclusion list, and honor every opt-out across every channel.
• Check the rules of each market before you choose the channel.

My team mostly targets the US, the UK and Europe, especially English-speaking markets. For Germany, Austria and the Czech Republic, we do not cold email without a compliant basis, and we use LinkedIn there instead.

This is not legal advice. For the email side in more depth, see whether scraping LinkedIn and cold emailing is actually legal.

Should you X-ray first or pay for a list tool?

Stay on Google when:

• you are testing a new niche, title or city, and 20 to 50 profiles will answer the question
• you want to see how buyers in one vertical describe themselves before you pay for a search
• you are spot-checking a list someone sold you

Pay as soon as any one of these is true:

• more than one rep will send from the list
• you need email addresses
• the list has to be refreshed every month
• you need filters for headcount or seniority
• you plan to contact thousands of people a month

The last point is the clearest trigger. Copying thousands of names a month out of Google by hand is slow, and keeping them current that way is slower.

That is the scale paid lists are built for. One UK social media marketing agency we worked with ran a full year on lists my team built in Sales Navigator, Apollo and UpLead, across 3 niches, with LinkedIn, warm emails and cold email. Booked calls climbed through the first quarter, 44, then 57, then 68, and reached 714 over the full 12 months.

A list of profiles also does not turn LinkedIn into a lead source by itself. IntoTheMinds ran an online survey of 500 companies in Germany, Belgium, the US, France and the UK on 15 and 16 December 2022. Only 34% said LinkedIn brought them leads frequently or very frequently (IntoTheMinds).

The data is from 2022. It measures how often leads arrive, and it says nothing about lead quality or about X-ray search.

If the paid seat is still the open question, read whether Sales Navigator is worth paying for on a small team.

Tomorrow, write one buyer-title string for your best customer type, run it for your home city, and score the first 20 profiles. If most of them pass, you have your titles, and the list can be built properly. If they fail, you changed the titles before paying for a list, and it took ten minutes.

If your X-ray search strings keep returning the wrong people and you want a second pair of eyes, book a call and bring one of the strings.