While low sample-efficiency is not a problem for easily computed scoring functions such as property calculation, it significantly hinders the use of scoring functions requiring a significant amount of computation such as molecular docking and computer aided-synthesis planning. This is becoming increasingly important with recent growth in interest in using molecular docking scoring functions to guide de novo molecule generation . This approach has shown to result in more diverse and novel compounds with a broader coverage of known active space than an equivalent QSAR model trained on known ligands . Other studies have used ML to model molecular docking or other physics-based scoring functions which is less computationally expensive . However, use of a model of a model reduces the advantages of such scoring functions by being less able to extrapolate novel chemical space and adds prediction uncertainty on top of pre-existing inaccuracies .