ML-assisted virtual screening. So far, we have seen that in a ∆-ML setting, the presented GPR models can lead to a modest increase in predictive performance relative to a semiempirical baseline method. This raises the question of whether this improvement has a tangible effect on the results of a HTVS for low-λ DFT molecules. To address this issue, we applied ∆K s , ∆K sp (each trained on 9600 molecules) and GFN1-xTB to screen 120.910 previously unseen molecules for promising candidates. For each model, we extracted 500 candidates with the lowest predicted λ and calculated their actual λ DFT values.