Effective modeling of retention times given the structure of analytes is possible in low data scenarios when utilizing transfer learning and GIN architectures. This capability of making accurate predictive models with a limited set of data is critical for effective application under practical conditions. We find that our approach is successful under these low data conditions for a small set of cyclic peptides, and that significant improvement compared to commercial software solutions is observed. With our final GIN model yielding an R 2 of 0.82 and average error being only 1.26% of the method gradient, we find this transfer learning approach is capable of repurposing old data for new tasks. Lastly, we hope that others find this application of GIN architectures and transfer learning for retention time prediction encouraging and investigate modeling on their own low data systems.