The use of artificial intelligence (AI) in drug discovery has increased rapidly in recent years, providing AI-aided design tools for drug design projects. The strengths of AI lie in finding patterns from vast amount of data from heterogeneous sources, at its best augmenting humans' abilities in challenging tasks such as molecular optimization. Advances in de novo molecular design tools enable automation of the design step in in silico design-make-test-analyze (DMTA) cycles of drug design , . They transform the task of a chemist from designing a molecule to designing a scoring function that is used to evaluate the generated molecules, and which essentially expresses the chemist's goal in a drug design project. Even though designing the scoring function may be an easier task for a human than coming up with new molecules, that is difficult, too, and the tools to date do not provide aid for this task. In the current practice, a design tool generates a batch of molecules, which are filtered and evaluated by a chemist, who consequently manually tunes the scoring function and its parameters to yield better generated molecules. This iterative process is laborious and requires broad expertise. Furthermore, even after automatic post-processing filters, the number of generated molecules is in the hundreds or thousands, an order of magnitude higher than the number of molecules humans can feasibly evaluate.