We then generated blind test sets from: a study on linear calibration of xTB-sTDA results (MOPSSAM), a study on singlet fission materials (INDT), a database of molecules relevant to green chemistry (VerdeDB), and a dataset to test the broad applicability of the model (PCQC-AL). On these external datasets, the ML calibration models outperformed both raw xTB-sTDA and linear calibration, oftentimes significantly. Averaging the best MAE over all external test sets (both S 1 and T 1 ) excluding PCQC-AL gave an MAE of 0.14 eV, compared to 0.38 eV for xTB-sTDA. Including PCQC-AL gave an average MAE of 0.57 eV, compared to 0.83 eV for xTB-sTDA. If xTB-ML is used as the first step in a highthroughput screening process instead of raw xTB-sTDA outputs, its low error can help ensure that all relevant molecules are selected, and vice versa.