In this manuscript the use of neural networks for expediting the purification of macrocyclic peptides by predicting their retention times associated with a standard gradient method is introduced. By predicting retention times for the standard method, a major experimental burden is alleviated leading to more streamlined identification of suitable purification gradients. This was achieved by applying a transfer learning approach utilizing graph isomorphism networks (GINs) , learning from a large retention time dataset collected under different chromatographic conditions, and predicting retention times for a small set of new compounds under standard method conditions (Figure ). This transfer learning approach outperformed the commercial solutions' underlying quantitative structural retention relationship modeling approach. We have also deployed these GIN models internally and have built a Dash app to facilitate usage by experimentalists. This study shows that highly predictive retention time models can be achieved by reusing existing data collected from unrelated studies.