When modeling the training set of 80 macrocyclic peptides using the commercial QSRR method, the fitted model was able to accurately model the training data. The QSRR model had a coefficient of determination (R 2 ) of 0.81, mean absolute error (MAE) of 0.136 min, and root mean square error (RMSE) of 0.197 min when concerning the training set. However, these results did not hold when using the QSRR model for predicting retention times on the test set. The QSRR model when applied on the test set of 24 analytes yielded markedly worse metrics, with R 2 , MAE, and RMSE being 0.11, 0.202 min, and 0.243 min respectively.