The random forest's top 250 predictions identified 37 of the 54 active compounds in the library. Here, we examine the applicability and performance of supervised learning models when operating on much larger libraries. Our machine learning pipeline selects a random forest model that successfully prioritizes compounds from over 8 million compounds from Aldrich Market Select (AMS). Then, we successfully apply the model on over one billion compounds in the Enamine REAL database, yielding a small, highly hit-enriched compound subset. These prospective tests demonstrate a cost-effective approach for navigating very large chemical spaces in the search for active compounds. The random forest model is easy to train and readily scales to current and future synthesize-on-demand libraries, unlike structure-based approaches.