A goal-directed molecule generator is typically paired with a scoring function, which reflects how closely a molecule resembles the desired profile. Unfortunately, finding a good scoring function is not trivial for many tasks, since this function should quantify biological effects of a molecule, together with synthetic feasibility and drug-likeness both of which are hard to quantify . Most scoring functions used in practice do not include the intuitive constraints that practitioners have. The scoring function might therefore be optimized by a molecule generator in a way that was not intended. This problem is intensified when using machine learning models as scoring functions, which we show in Section 3.