We used Enamine REAL to demonstrate how our supervised learning workflow can scale to a much larger synthesize-on-demand chemical library, maintain high predictive accuracy, and recover potent active compounds. Scoring all 1,077,562,987 compounds in the library with the RF-C model required only tens of hours when parallelized on 18 CPU cores with modest resources (Appendix B.3 and Table ). We sorted all compounds by their RF-C score and requested a quote for the top-ranked 10,000 compounds. 5,620 of the 10,000 could be delivered in less than a month, and we discarded the rest. most similar active ranging from 0.35 to 0.62 (Figure ).