Horizontal clusters of compounds in the prediction of the subset with unknown features suggested that the trees in the GBT model did not know how to split some samples and therefore predicted similar 19 F NMR chemical shifts for them. The features that would have facilitated this split appeared to have not been in the training set. The higher density of test cases in the unknown features subset in the -130 ppm to -230 ppm range reflected the expansion of the LEF4000 to the LEF5500 and its explicit goal to expand into new chemical space, by embracing alkyl fluorinated motifs. Table in the supporting information shows that the unknown features subset was predicted with almost 5-fold higher MAE and RMSE than the known features subset. It was obvious that the unknown features subset was outside of the applicability domain of the GBT model and required an orthogonal method.