Separate versions of all models were built for the three fluorine motifs: CF, CF2, and CF3. Models were selected based on their hyperparameter optimized performance when trained on the LEF4000 data set. Not all models were parametrized using cross-validation, but all parametrized models were eventually run in a cross-validation scenario for selection. The regularization parameter of Lasso regression was optimized using coordinate descent as implemented in the scikit-learn Python package. KNN regression, RF, and GBT model parameters were optimized using a grid search and evaluated in 5-fold cross-validation. Chemprop models were hyperparameter optimized using the provided method based on Bayesian optimization, which internally performs 80:10:10 splits. All hyperparameter configurations can be found in Table of the supporting information.