The strong results in our prospective evaluation of PriA-SSB inhibitor predictions support the potential for similar supervised learning-based virtual screening in other drug discovery campaigns. The random forest model we selected generalized well to the AMS library of over 8 million compounds and the synthesize-on-demand Enamine REAL library with over a billion compounds. These putative active compounds require further validation, such as testing in our fluorescence polarization assay as a confirmatory screen. The presence of false positive compounds in the training data, such as AlphaScreen frequent hitters, may have led to prioritizing similar false positives in the AMS and Enamine REAL collections.