Our first insight is to model the chemist's goal via probabilistic user-modeling, to automatically adapt the scoring function to match their goal. The adaptation is done by querying a chemist for feedback on molecules and using the feedback to estimate the parameters of the desirability functions of each molecular property to be optimized in Task 1, or in Task 2 for fitting a nonparametric predictive model for single-parameter optimization. We show empirically that a scoring function adapted in this way will yield molecules that better match the chemist's goal.