The trained models were used to predict on the Freesolv-SAMPL4 test set. Because low errors in training validation do not necessarily translate into low errors in testing validation, all trained models were tested (see figure and table ). Top-performing models per ML model (see figure ) were based primarily on the MolProps feature set for SVM, RF and MLR, but not for DNN. It is likely that the latter suffers from a degree of overfitting causing individual models to differ widely in predicted offset values. This is apparent in the much larger uncertainties in dataset metrics for DNN. Nevertheless the accuracy of the predictions obtained by averaging over the 50 DNN models is competitive. Overall SVM appeared to give more consistently accurate and precise estimates of ∆G of f set values.