GuacaMol was a seminal first benchmark, which provided a standardised training dataset (for a generative model to use for pre-training/seeding, if applicable) and a suite of 20 objectives for goal-directed generative models to optimise. These tasks all measure the similarity to one or more reference compounds, where the method of measurement differs across objectives. This benchmark also introduced a suite of performance metrics to aid evaluation of de novo molecules. Although a few tasks were challenging (~5/20), the rest are easily solved by generative models (including GraphGA and SMILES-LSTM ) and are therefore not suitable to adequately separate top-performing generative models (as also stated by the authors). Furthermore, despite the implementation of several scoring functions and score transformations, modification of the code was required to create a custom task with the functionality present. More recently, Gao et al. applied some of the GuacaMol objectives and re-framed them into a sample efficiency benchmark, significantly extending the evaluation of generative models to 25 approaches; however appropriate evaluation of the chemistry generated was lacking . All of the benchmark objectives in GuacaMol can be reimplemented in MolScore without requiring any coding (configuration files in Supplementary Information) and all transformation functions and most performance metrics are integrated.