A real-world interpretation of generative models in the drug discovery context remains difficult, and the current work attempts to better understand this by retrospectively applying performance measures to generative models applied to public and private drug discovery data sources. The objective of the task is hence to achieve late-stage project compounds, given information from early-stage compounds, in a limited number of steps, and hence in a sample-efficient way (for a more detailed recent evaluation of the sample efficiency of different methods see a recent study ). This early/late data split strategy is in analogy to 'time-split' validation in the QSAR area, where splitting data into training and test sets along the time domain has been proposed before .