While this leads to a significant improvement of the predictive performance compared to the baseline, we find that the benefits of this are actually somewhat marginal in the context of virtual screening. Specifically, ML enhanced screening is more effective in identifying promising candidates, but the semiempirical model actually has some advantages in terms of candidate diversity. This calls into question whether the cost of building the ML models (in particular the generation of training data) is actually justified. To obtain a clear advantage, more accurate and/or data-efficient models are thus required.