Using the rich RepoRT chromatographic metadata, we explored the relative performance of Graphormer-RT as a function of chromatographic parameters. For RP methods, Graphormer-RT performs best for methods that employ MeOH as the organic phase rather than ACN, low flow rates, low t0, and Waters columns. HILIC methods showed no obvious bias in model performance with respect to chromatographic conditions, possibly due to the relatively small dataset size. Ablation studies showed that the best performing Graphormer-RT architecture consisted of a split (by datatype) "chromatographic" encoder. We also found that gradient features were more important for producing accurate model predictions than column features, though both are required for optimal model performance. Finally, although removing empirical chromatographic method data diminished model performance, Graphormer-RT still performed reasonably well, possibly due to its robust understanding of how molecular structure affects interaction strength.