The mean and median Tanimoto similarity between each member of the test set and its closest neighbor in the training set was calculated using Morgan Fingerprints (bits=1024, radius=2) to quantify the difference between the two splits. This resulted in a mean similarity of 0.90 and a median of 1.00 for the random-stratified split, indicating that the chemical space of the test set is not significantly different from the training set in this split. Since a Tanimoto similarity of 1.00 means that the two molecules in question are identical, further analysis revealed that 776 molecules in the data set also existed in the training set with different experimental values on other targets. This meant that over 50% of the molecules in the test set were already covered by the training set, but with different targets. In contrast, the realistic split resulted in a mean similarity of 0.48 and a median similarity of 0.52, demonstrating the effectiveness of realistic splitting in producing a slightly more chemically novel test set. To optimize the hyperparameters for the realistic split models, we performed a 10-fold cross-validation. Linear sampling was used without shuffling the data, ordered by cluster number from largest to smallest. Therefore, with this method, each fold tested was the most dissimilar to the rest of the training set. The PCM models performed significantly worse on the realistic splits than on the randomly stratified splits (Table ). However, the metrics were still in the realms of powerful predictive ability, according to Grisoni et. al, having Q 2 over 0.30. Overall, there were no significant differences between the algorithms used, namely RF and XGBoost.