Our study considered only a limited set of machine learning models, hyperparameters, and chemical representations. These choices were partially influenced by our prior experience modeling PriA-SSB inhibitors. Although Sheridan et al. previously identified settings for four XGB hyperparameters that performed well across a variety of QSAR tasks, we performed a more extensive hyperparameter search over 14 XGB-C hyperparameters instead of using their recommendations. More notably, we did not include any graph neural networks. Graph neural networks, ranging from convolutional neural networks on the molecular graph to more advanced graph transformers are appealing because they do not require precomputing chemical representations such as Morgan fingerprints. Expanding the set of machine learning models, hyperparameters, and chemical representations evaluated could have affected our retrospective cross validation results and led to the selection of a different model. However, our primary goal was not model benchmarking but rather studying the prospective performance of the best model selected by cross validation in large commercial libraries. Future studies that include more machine learning models during cross validation may select even stronger models that provide better prospective performance than we observed.