Our second contribution is to present how Bayesian optimization, a well-established machine learning method, efficiently chooses which molecules are shown to the chemist for the interest of adapting the objectives better and generating high-scoring molecules. From the methodological point of view, this work provides a proof of concept for interactive reward elicitation in drug design -that is, how to actively learn about the reward function of reinforcement learning by interacting with a human. We first show the effectiveness of the methods in simulated example cases, and then demonstrate the performance with a human chemist's feedback using a graphical user interface for interaction with the system.