Each optimized model was defined as the combination of an algorithm and a fingerprint, whose hyperparameters were optimized using LOO-CV, with balanced accuracy (BA) employed to select the best hyperparameters. To estimate the confidence of the predictions as they differ from the training set, the bestperforming models were also compared in terms of their precision and recall computed on a distance-tomodel (DM) basis using Morgan fingerprints. For that, predictions were categorized into four quartiles according to their mean Jaccard distance to the compounds in the training set. Having the confidence estimation, these models were retrained in the entire dataset and applied to predict the activity class of the compounds generated by de novo design.