Our primary goals were to identify a strong supervised learning model for predicting PriA-SSB inhibitors and then prospectively test the model in a large-scale virtual screen on the AMS and Enamine REAL libraries that contain 8,187,682 and 1,077,562,987 compounds, respectively. As a first step, we used a PriA-SSB high-throughput screening training dataset with 427,300 compounds and 554 actives to compare multiple types of supervised learning algorithms, optimize their hyperparameters, and select the top-performing model. We included classification models (denoted with -C) trained to predict binary activity as well as regression models (denoted with -R) trained to predict continuous % inhibition and identified a random forest classifier (RF-C) as the best model. Compounds in the training dataset were tested at 33.3 µm. Binary activity labels for the classification models were assigned based on an activity threshold of ≥ 35% inhibition (3 standard deviations in the % inhibition distribution).