The use of a common test set enables the direct comparison of the performance of the active learning approach with that of the baseline model. For recollection, the baseline model yielded an MCC of 0.50 on the test data. The active learning approach reached comparable performance To confirm the added value of the active learning approach over random sample selection (where a random atom instead of the most informative atom is added to the training set during each iteration), we compared the curve progression of the two sample selection strategies. Figure and Table show that active learning indeed produces better models with smaller data sets than the random selection approach: When using 6100 data points (25% of the active learning data), the MCC values of the models generated with the two approaches were 0.49 and 0.40 for the validation sets, respectively, and 0.48 and 0.40 for the test set, respectively. The Top-2 success rates followed the same trend as the MCC values on the test set; only the improvement rates of the AUC values were comparable between the active learning and the random selection approach.