To provide a more fine-grained overview of the KS values across descriptors and targets beyond the analysis of general trends, we performed a principal component analysis (PCA). For each dataset size (10 and 100) and similarity level (high, low), the results were described in a tabular form, with each augmentation approach applied to a target being a row, described by 24 KS values (eight descriptors for each targets, across three targets, in comparison to the fine-tuning set) as the columns. As in previous studies, to improve interpretability, we added two additional rows: 'best' and 'worst', corresponding to the minimum and maximum KS values obtained in each column, respectively. This addition 'stretches' the variance explained by the first component in the best-worst direction, so that, the closer a method is to 'best' along the best-worst direction, the better it performs on average across descriptors (Fig. ). Deviations from the best-worst line represent descriptor-and target-dependent variability. Except for the scenario with 100 low-similarity fine-tuning data (Fig. ), at least one augmentation method outperforms SMILES enumeration on average (Fig. ). In these cases, random masking is the best performing method (and the second best in the remaining case, Fig. ). The relative performance of the other methods (except for self-training performing consistently poorly) depends on the case study (Supplementary Table ), with no evident trends. These results underscore the potential of random atom masking for distribution learning, and the need to investigate the usefulness of the other approaches on a case-by-case basis.