Even though deep learning has been dominating de novo drug design in the recent years, it should be noted that the field also has a long history of evolutionary heuristic methods such as genetic algorithms on the forefront . These traditional methods are still being investigated and developed and it is yet to be established how they compare to the novel approaches based on deep learning . Due to the simpler nature of these traditional approaches non-obvious relationships can be easily missed, which may affect the quality of the suggested chemical structures. However, simplicity can also be an advantage since interpretation of simpler methods is easier. This is especially problematic for deep learning models that can have more than thousands of parameters . Moreover, a simpler method requires less training data without sacrificing chemical space coverage .