ML-based workflows using the rooted fingerprint achieve high correlations in the prediction task. Even though the MAEs are still orders of magnitude higher than achievable experimental errors , the predictions are already useful in application. ML workflows are however expected to fail in situations when the predicted compounds contain features that are not covered by the training set. Some ML architectures managed to extrapolate better than others. Previously used models such as KNN were outperformed by more complex models. On the other hand, Chemprop was also outperformed, probably because the size of the training sets could not yet justify a deep-learning model. All models incurred a significant number of strong outliers. In these cases, a model derived from physics in the form of a QMbased workflow is more successful. The given QM workflow was able to generalize better in predicting 19 F NMR chemical shifts for fluorines in new local environments and was largely independent of a training set. It was on the other hand less precise on in-distribution samples compared to the ML workflow. Handling out-of-distribution samples by QM and in-distribution samples by ML led to better prediction performance and significantly fewer strong outliers. A few limitations and possible extensions of both the ML and QM methodology are discussed in the following paragraphs in more detail.