  De novo molecule generation can suffer from data inefficiency; requiring
large amounts of training data or many sampled data points to conduct objective
optimization. The latter is a particular disadvantage when combining deep
generative models with computationally expensive molecule scoring functions
(a.k.a. oracles) commonly used in computer-aided drug design. Recent works have
therefore focused on methods to improve sample efficiency in the context of de
novo molecule drug design, or to benchmark it. In this work, we discuss and
adapt a recent sample efficiency benchmark to better reflect realistic goals
also with respect to the quality of chemistry generated, which must always be
considered in the context of small-molecule drug design; we then re-evaluate
all benchmarked generative models. We find that accounting for molecular weight
and LogP with respect to the training data, and the diversity of chemistry
proposed, re-orders the ranking of generative models. In addition, we benchmark
a recently proposed method to improve sample efficiency (Augmented Hill-Climb)
and found it ranked top when considering both the sample efficiency and
chemistry of molecules generated. Continual improvements in sample efficiency
and chemical desirability enable more routine integration of computationally
expensive scoring functions on a more realistic timescale.
