RDKit descriptors and Morgan Fingerprints for both training set compounds and virtual screening library were calculated using the RDKit package in Python. Simplex descriptors were calculated with the HiT QSAR program . Machine learning models were developed using the Python packages scikit-learn and PyTorch (for neural networks). Scikit-learn models used the default hyperparameters. All models were trained as classifiers (using binary labels) but produced a "confidence score" by different means. These "confidence scores" were averaged during consensus prediction calculation. Since the Simplex descriptors are computationally expensive , to calculate consensus predictions on the full test set, only the descriptor-based models with RDKit descriptors and Morgan Fingerprint were run to calculate consensus predictions on the full test set. Subsequently, the top 2,000 scoring combinations were evaluated with the Simplex-based models and the models with IC50 data.