Identifying the chemical regulators of biological pathways is a time-consuming bottleneck in developing therapeutics and research compounds. Typically, thousands to millions of candidate small molecules are tested in target-based biochemical screens or phenotypic cell-based screens, both expensive experiments customized to each disease. Here, our uncustomized, virtual, profile-based screening approach instead identifies compounds that match to pathways based on the phenotypic information in public cell image data, created using the Cell Painting assay. Our straightforward correlation-based computational strategy retrospectively uncovered the expected, known small-molecule regulators for 32% of positive-control gene queries. In prospective, discovery mode, we efficiently identified new compounds related to three query genes and validated them in subsequent gene-relevant assays, including compounds that phenocopy or pheno-oppose YAP1 overexpression and kill a Yap1-dependent sarcoma cell line. This image-profile-based approach could replace many customized labor- and resource-intensive screens and accelerate the discovery of biologically and therapeutically useful compounds. Display omitted •Compounds impacting particular genes’ function are highly sought•Most chemicals and overexpressed genes impact cell morphology in the Cell Painting assay•Matching these image profiles can find chemicals that impact a particular gene’s function•This virtual screen using public data found new chemical regulators of several pathways If a chemical compound and a gene overexpression yield the same cell morphology in the microscopy-based assay Cell Painting, then they are likely to impact the same functions. This principle is exploited to retrieve useful compounds for particular query genes in public Cell Painting datasets.