This work presents the first proof-of-principle for using human-in-the-loop interactions to aid de novo molecular design. We studied two approaches which we envision captures the basic usecases where interactive machine learning provides a more principled way to exploit chemist's knowledge than manual trial and error. The first approach of tuning the parameters of available reward function components is likely to be useful especially for new users of de novo tools by reducing the mental load of planning which scoring function captures their goal. On the other hand, the second method, which builds a scoring function from scratch, could benefit the start of new projects, where experimental datasets may exist but are very small, and we wish to augment them with chemists' intuition.