To test the stability of the active learning approach with respect to the initial pair of atoms (one SoM and one non-SoM) selected for starting off the iterative modeling process, we repeated the model building process five times (keeping fold 1 as the validation set for all five runs). As expected, and shown in Figure and Table , the differences between the individual runs with different initial atom pairs were marginal and became even smaller as further data was added to the training set. After approximately 6100 iterations already (representing 25% of the active learning data), the standard deviations of the MCC values were smaller than 0.01 in all cases. The high stability of the active learning approach is also reflected by the fact that a substantial proportion of the atoms present in the active learning set were picked for training during each of the five repeats. For example, by the time 25% of the atoms in the active learning set were selected for training, the number of atoms consistently selected during each of the (five) repeats of the experiment corresponded to 55% of the samples in the training data (Figure ). The percentage of atoms consistently selected for training increased further with the progress of the active learning process. Based on these observations we conclude that the active learning protocol is highly robust and yields consistent, good results that are largely independent of the starting conditions.