Here, we demonstrate that supervised learning approaches commonly used for ligandbased virtual screening are highly scalable and capable of strong prospective performance in very large chemical libraries. We build upon our prior virtual screening effort to find inhibitors of a bacterial protein-protein interaction between PriA and single-stranded DNA binding protein (SSB). SSB's interactions are critical for maintaining prokaryotic genome stability and as such represent potential antibiotic targets. Our previous effort involved the prospective evaluation of a random forest model on a library of 22,434 compounds.