Badass Boolean: A Real Tab That I Practice in My Daily Work As a Sourcer

Real Boolean strings, pulled directly from an active sourcing workflow, show how experienced sourcers build search logic for sales, healthcare, and tech roles rather than relying on theory or generic templates.

The core framework rests on three operators: OR for inclusive searches (any data point present), AND for exclusive searches (all data points required), and NOT for removing noise from results, such as job postings that clutter a search meant to surface candidates. Searching a company's own ATS and CRM comes first, since those people have already expressed interest and cost nothing to reach. Saving strong candidates from every search into a CRM, even before they apply, builds a reusable pool for future roles.

Several worked examples cover:

• Sales strings that pair phrases like "solution selling" with variations such as "B2B sale" or "B2B sales," accounting for how people actually phrase things on a resume rather than assuming one standard term.
• A healthcare executive search that combines title, company, and degree strings separately, then merges and deduplicates the results, including permutations like "M.S.," "master's," and "MSCS."
• A cardiothoracic nursing search built after a kickoff call with a hiring manager, adding spelling variants such as "cardiac thoracic" and "cardio-thoracic" once they turned up in candidate profiles.
• Tech strings for data scientist, UI/UX designer, and .NET developer roles, showing how adding a single required tool, such as a full creative software suite instead of one program, sharply narrows the candidate pool.
• A diversity-focused search built around name patterns, framed as a client directive applied before candidates ever self-identify through an application form.

Nesting parentheses gets explained through an audience question: grouping phrases with OR inside parentheses, then combining that group with AND against other required terms, lets a single string demand one phrase from a list alongside separate required elements. Company target lists often come from a sales intelligence tool, pulling accounts similar to an existing client by industry and geography, then merging lists from multiple sources.

The overall approach favors running several narrower searches, on title, company, and degree, separately, then combining and deduplicating results rather than chasing one long string meant to solve everything. A single query rarely uncovers the full hidden talent pool; layering different tools and search angles does.