Distribution-learning models can be used to generate molecule libraries or can serve as a starting points for goaldirected generation. Evaluation of these models can be problematic as demonstrated by recent efforts to establish benchmarks for their evaluation ). These benchmarks often comprise heuristics that try to capture desired prop-erties of the generated molecules; for example, measuring whether the distributions of certain molecular properties match. Many of these heuristics can however be tricked by naive models as we show in Section 2.