Moreover, high, outlying SAscore results suggest that currently, pure ML techniques still do not replace completely a human mind in the retrosynthesis planning process. This implies that the accuracy of scores, although increasing, is still limited. This results in a constant need for improving the quality of training datasets, because ML models may overfit to specific properties of training datasets that appeared to be unbalanced or biased. Also, there should be constant pressure for better model design. We conclude that hybrid ML and human intuition-based synthetic accessibility scores with carefully crafted retrosynthesis planning algorithms can still efficiently boost the effectiveness of computer-assisted retrosynthesis planning. These tools may help for both finding synthetic routes of newly designed compounds as well as recognizing what is still unknown in chemistry.