In general, the datasets introduced in the MF-PCBA-60 collection do not appear difficult in terms of the roughness of the chemical space, with ROGI values ranging from 0.014 to 0.571, an average of 0.167±0.135 (± standard deviation), and most of the datasets scoring below 0.4 (see Supplementary Figure for a histogram). The performance metric (R 2 ) is not correlated with the ROGI for the RF, SVM, and deep learning non-augmented (base) models (Figure and Supplementary Figures and). However, it is possible to observe statistically significant weak, negative correlations for the augmented RF and SVM models (Figures and and Supplementary Figures and). The relationship between the ROGI and the ∆R 2 is not significant for any model at a significance level of 0.05 (Figures and Supplementary Figures ), but in the case of SVM and deep learning the p-values are close, especially in the case of deep learning (p=0.05042). The highest uplifts in performance are observed for datasets with with relatively low ROGI (< 0.3), with extremely small or even negative performance differences for the highest difficulty datasets (ROGI > 0.5). Finally, multiple linear regression models did not indicate significant contributions from the ROGI (Supplementary Information 8).