We also evaluated the impact of training set size on the accuracy of the correction terms (figure ). Hyperparameter configurations were taken from top performers in the training phase of this study (see table ), and increasingly large, randomly sampled subsections of To put these results in perspective in the context of SAMPL4, the changes in ranks of the FEP/ML entry was plotted as a function of training set size (figure ). FEP/ML models outperform standalone FEP for all statistical measures, although some variability is observed. Whereas MUE and Kendall τ already show clear improvements from small training set sizes (ca. 100 and 50, resp.), Pearson r and RMSE appear to require models trained on a larger number of compounds to reach placement in the top five ranks of the SAMPL4 challenge (250 and 500, resp.).