We propose to assist this manual trial-and-error approach in designing the scoring function by interactive human-in-the-loop machine learning. Human-in-the-loop learning (HITL) is a branch of machine learning where human users can interact with a machine learning model during model training and usage, to integrate expert knowledge to the model and improve the model's performance - . In molecular design, recent studies have found that medicinal chemist's intuition can perform on par with machine learning methods e.g. in solubility prediction , but to date the two approaches have not been combined. The HITL approach introduced in this work provides a principled way for integrating human intuition into de novo molecular design.