The results of the SAscore may seem surprising. It is a slightly different score from the rest because it is not a standard ML model. It is designed as a combination of scores and penalties derived by experts from the presence of structural fragments in the PubChem database. From this, we infer that in retrosynthesis, human intuition and the power of the human mind still play an important role in planning a synthesis route especially in noticing the irregularities in the general synthesis rules. On the opposite, ML models are prone to imperfections, imbalance, bias, or gaps in training data. This lies in line with recent studies indicating ML limits in cheminformatics, for example for reaction yield prediction , for CADD , or for graph-based DL models for drug representation . We checked also if synthetic accessibility scores can model the complexity of the retrosynthesis planning. We computed a Spearman rank correlation between scores of target compounds and their search tree complexity parameters, such as treewidth, number of nodes, and number of not solved leaf nodes. Results are available in Figure . All of RAscore, SAscore, and SCScore with at least one node aggregating statistic correlate negatively with all complexity parameters with significance below 0.04. On the contrary, SYBA does not correlate with any of the complexity parameters. Analogously as earlier, RAscore and SAscore performed best, the strongest negative correlation was observed between these two scores and the number of nodes.