The methods were analysed for their ability to learn the distribution of the selected molecular properties, measured via Kolmogorov-Smirnov (KS) distance (Supporting Table ). In general, distribution learning was more effective when 100 and/or dissimilar fine-tuning were used (Fig. ). All augmentation methods (except for self-training) significantly outperformed no augmentation (Wilcoxon signed-rank test, p-value < 0.02) and performed on par to SMILES enumeration when 10 highly similar fine-tuning molecules were used. Moreover, random atom masking significantly outperformed SMILES enumeration (Wilcoxon signed-rank test, p-value < 0.002). In low similarity scenarios, most methods significantly outperform no augmentation (p-value < 0.002) and perform similarly to SMILES enumeration for finetuning sets of 10 molecules (Wilcoxon signed-rank test, a = 0.05) when general trends are analysed.