  Bayesian optimization (BO) is a principled approach to molecular design
tasks. In this paper we explain three pitfalls of BO which can cause poor
empirical performance: an incorrect prior width, over-smoothing, and inadequate
acquisition function maximization. We show that with these issues addressed,
even a basic BO setup is able to achieve the highest overall performance on the
PMO benchmark for molecule design (Gao et al 2022). These results suggest that
BO may benefit from more attention in the machine learning for molecules
community.
