search_companies is how you build and qualify an account list before you go looking for people at those accounts. It shares most of its vocabulary with search_people, so filters you learn here transfer directly.
Filters are free precision
Search cost is driven by the free-text query, not by the filters attached to it.
Query shape | Relative cost to execute |
| Baseline |
| The same — filters add nothing |
Filters alone, no | ~40× cheaper |
Two rules follow.
Never send a bare
query. Addingindustry,employee_range, orrevenue_rangecosts nothing and sharply narrows the result set. A query-only call is the least precise shape available and no cheaper than a filtered one.Drop
queryentirely when filters can carry the whole intent. Most account lists are pure firmographics — “software companies with 51–200 employees and 10M–10M–20M revenue” needs no free text at all, and runs ~40× cheaper without it.
{ "industry": "Computer Software", "employee_range": "51-200", "revenue_range": "$10M - $20M" }Reach for query only when you’re matching a company by name. Everything else belongs in a filter.
This is about execution cost, not billing. Tokens are charged per record returned regardless of query shape — a cheaper query returns faster and puts less load on search capacity, but it doesn’t change what you’re charged.
Use domain when you have one
If you know the website domain, domain is an exact match and always beats a free-text query:
{ "domain": "acme.com" }Full URLs are normalised automatically — https://www.acme.com/about becomes acme.com. There’s no need to strip the protocol or path first.Reach for query only when you have a name and no domain. It searches the company name only; domain and industry are keyword fields and aren’t covered by it.
Choose your industry granularity
As with people search, three filters describe industry at different resolutions:
Filter | Vocabulary | Use when |
| 23 primary buckets plus long-tail values | You’re thinking in broad categories |
| LinkedIn’s own ~150 labels | You need a niche the buckets can’t express |
| Full NAICS hierarchy | You want standardised codes, or precise control over breadth |
The industry enum also carries long-tail values — "Insurance", "Banks", "Computer Software", "Pharmaceuticals and Biotechnology", "Aerospace and Defense", "Electronics", "Mining and Metals". These overlap conceptually with the broad buckets, so a company in computer software may be filed under either "Information Technology" or "Computer Software". If a search comes back thinner than expected, try the neighbouring value.
naics_code matches at any hierarchy level. Use a 2-digit sector ("23" = Construction) to cast wide, a 6-digit code ("511210" = Software Publishers) to go narrow. Shortening the code is the cleanest way to widen a search that’s returning too little. See Understanding NAICS Codes for more information.
Understand the location caveat
country and state filter the company’s location — but that location is derived from where the company’s team is based.
This is accurate for the small companies that dominate the index, but large multinationals are attributed to a single one of their offices. A global company headquartered in one country may resolve to another entirely. When targeting large or international companies, combine location with other filters rather than relying on it alone.
Both require exact stored values with standard capitalisation — "Texas", not "TX". Combine state with country for precision.
Watch the size and revenue distributions
Both range filters are exact-match, and both have a heavily skewed distribution.
employee_range — "1-10" is the most common value by a wide margin. A search filtered to small bands will return a great deal; one filtered to "5001+" will return very little.
Value | Reads as |
| Solo, micro, very small — most common by far |
| Tiny team |
| Small startup |
| Small-mid |
| Mid-size |
|
|
| Large |
| Enterprise, very large |
Legacy values "Small", "Mid-Market", "Enterprise", and "Unknown" also exist. Prefer the numeric ranges — they carry far more coverage.revenue_range — "$500k - $1M" is the most common band.
"$1 - $1M" is a legacy format covering the same range as "$500k - $1M" but holding far fewer records. Always prefer "$500k - $1M", or you’ll miss most of the band.
Identify companies from traffic
ip_to_company resolves an IPv4 address to the domain of the organisation behind it — useful for de-anonymising website visitors and triggering account-based workflows.
{ "ip_address": "8.8.8.8" }This is a B2B tool. Consumer ISP addresses will typically not resolve to a named company, so expect a meaningful miss rate on general web traffic.
Chain company search into people search
The most common workflow is two calls: qualify accounts, then find contacts at them.
search_companieswith your firmographic filters → gives you a list of domainssearch_peoplewithcompany_domainset to each domain → gives you contacts
const companies = await search("search_companies", {
industry: "Computer Software",
employee_range: "51-200",
revenue_range: "$10M - $20M",
limit: 25
});
for (const company of companies.results.results) {
const people = await search("search_people", {
company_domain: company.domain,
seniority: "VP",
limit: 25
});
}search_people can also filter on employee_range and revenue_range directly, applied to the person’s current employer. If firmographics are the only reason you’re calling search_companies, you can often skip it and filter in one call.
Paginate deliberately
Results are ranked by _score, so the first page is the strongest.
| Limit |
Results per call | 25 max, 10 default |
Results per call (Free plan) | 5 max |
Free plan lifetime search records | 100 |
Read results.total before walking pages, then increment offset by your limit.