Synthesize-on-demand libraries can provide access to many more hits and higher quality hits with respect to chemical diversity and other desirable properties, as exemplified by the potent PriA-SSB inhibitors we identify. Our simple supervised learning approach has a high hit rate, good chemical diversity, and can scale to synthesize-on-demand libraries. Scoring over one billion compounds takes less than 1,000 CPU hours and can be trivially parallelized to score even larger chemical libraries in the future. In contrast, the computational costs of structure-based scoring of a library of this magnitude are significant if not prohibitive.