We test the performance of the fingerprint and graph representations with five different models. Namely, Random Forest (RF), Feed-Forward Neural Network (FF-NN), light gradient boosting machine (LightGBM ), a simple Graph Convolutional Neural Network (GCNN) and the Message Passing Neural Network (MPNN) by Gilmer et al. . The Gilmer MPNN is identical to the one used by Kneiding et al. , the difference being that we featurize our graph representations based on RDKit properties that can be obtained directly from our SMILES. For additional details on ML training see section S1 in the SI.