The results show a strong correlation between QM-calculated and The results clearly show that the most reliable uncertainty estimates are obtained using the random forest standard deviation as seen in the Supporting Information. The error-based calibration plots for the MCA and MAA held-out test sets using the random forest standard deviation are shown in Figure highlighting a direct relationship between the RMSE and RMV with R 2 > 0.99. The linear regression equations in Figure can be used to convert the predicted uncertainties from the random forest model into an estimated error of the LightGBM model predictions. However, as the data is binned according to the uncertainty, the results only reflect the general trend of a higher predicted uncertainty leading to a higher probability of a large predicted error. Thus, a high predicted uncertainty can still result in a low error. The main objective of the uncertainty predictions is therefore not to accurately estimate errors, but rather to serve as a tool for identifying potentially incorrect property predictions, where it would be advisable to run the QM-based workflow. To achieve this, we define estimated error cutoffs based on the amount of QM calculations we allow to compute for the held-out test set. For estimated errors above 25 kJ/mol for MCA and 30 kJ/mol for MAA, we permit around 10 % of the held-out test set with the highest predicted uncertainties to be processed using the QM-based workflow. Returning to the data points with true errors exceeding 100 kJ/mol, we can identify 110 out of 136 outliers for MCA (81 %) and 164 out of 241 for MAA (68 %) using these estimated error cutoffs. Reducing the cutoffs to 20 and 25 kJ/mol (∼ 25 % of the held-out test set) will increase the detection rate to 93 % for MCA and 82 % for MAA, while cutoffs of 15 and 20 kJ/mol (∼ 45 % of the held-out test set) allow the detection of 100 % for MCA and 92 % for MAA as seen in Figure . To explore the limits of our ML models, we now evaluate them on a challenging non-druglike dataset from Tavakoli et al. containing both neutral compounds and compounds with a non-zero formal charge (see Figure ). The ML models suffer from large errors for the small molecules with a non-zero formal charge (i.e. protonated or deprotonated molecules), which can be explained by the fact that the ML training sets only contain neutral druglike molecules. However, we can detect most of these data points using the error estimates with cutoffs of 25 and 30 kJ/mol for MCA and MAA, respectively, and all of them with strict cutoffs of 15 and 20 kJ/mol for MCA and MAA, respectively. The Pearson correlation between the QM-calculated and ML-predicted MCA and MAA values goes from 0.68 to 0.99 for MCA and 0.82 to 0.95 for MAA. This further validates the ability to reliably identify outliers and shows that lower estimated errors reflect greater confidence in the predicted properties.