In an effort to develop a rational approach to identify anti-cancer agents with selective polypharmacology, we mine millions of docked protein-ligand complexes involving more than a thousand cancer targets from multiple signaling pathways to identify new structural templates for proven pharmacophores. Our method combines Support Vector Machine-based scoring to enrich the initial library of 1,592 molecules, with a fingerprint-based search for molecules that have the same binding profile as the EGFR kinase inhibitor erlotinib. Twelve new compounds were identified. In vitro activity assays revealed that three inhibited EGFR with IC(50) values ranging from 250 nM to 200 µM. Additional in vitro studies with hERG, CYP450, DNA and cell culture-based assays further compared their properties to erlotinib. One compound combined suitable pharmacokinetic properties while closely mimicking the binding profile of erlotinib. The compound also inhibited H1299 and H460 tumor cell proliferation. The other two compounds shared some of the binding profile of erlotinib, and one gave the most potent inhibition of tumor cell growth. Interestingly, among the compounds that had not shown inhibition of EGFR, four blocked H1299 and H460 proliferation, one potently with IC(50) values near 1 µM. This compound was from the menogaril family, which reached Phase II clinical trial for the treatment of lymphomas. This suggests that our computational approach comparing binding profile may have favored molecules with anti-cancer properties like erlotinib.