Goal-directed generative models for molecules are trained to produce molecules with some desired property profile, for example physical or chemical properties, bioactivities or a combination thereof. Many of such models were tested on their ability to generate compounds with a high penalized logP score . It should however be noted that it is trivial to achieve stateof-the-art results by generating long saturated hydrocarbon chains. The GuacaMol package ) was an important step in improving evaluation. We agree with the authors, however, that the benchmarks are easy to solve and that the quality of generated compounds is insufficiently addressed.