The reliability of the different approaches can be influenced by the dataset used. As such we also tested their behaviour on the datasets compiled by Gao and Coley that include typically used catalogues of compounds as well as de novo generated molecules for which retrosynthesis predictions were computed (see Methods). Overall, the SAscore and the RAscore better reproduced the retrosynthesis results (see Supplementary Information Section 4), but the different data subsets show quite different results. One case of especial interest is the dataset of de novo generated molecules that were optimized against several multiproperty objective functions (see Supplementary Information Figures ). In this case, the RAscore score performance drops when the properties used to optimize the molecules do not account for SA. These results are in line with what would be expected for a machine learning approach, since the molecules that are obtained, although biased to replicate catalogue properties, do not necessarily represent viable instances. The results for the PC and NPNP dataset and those from the Gao and Coley datasets suggest that the SAscore, with all its imperfections, is currently the best heuristic for retrosynthesis-based SA estimation. However, and leaving aside that synthesizability may not be the most useful proxy for compound availability, there are also several examples reported where SAscore severely underperforms (for visual examples see Supplementary Information Figure ). Moreover, retrosynthesis-based methods, despite being computationally demanding, are not perfect at identifying synthetically accessible compounds. The high degree of variability and the fact that the agreement between the different estimations depends on the dataset used, suggests that all methods are far from perfect (see Supplementary Information Figure ).