To explore model generalizability, we held-out six methods (i.e., 3 RP, 3 HILIC) from our training dataset. For four of these six methods, we find that Graphormer-RT generalizes reasonably well, yielding MAEs of less than one minute. However, Graphormer-RT completely failed to generalize for a complex gradient method (MAE = 596.8 ± 12.7 s), suggesting that a more complex encoding scheme may be needed in such instances. Encouragingly, though, including 80% of the RTs for this method in the training set led to a substantial improvement in model accuracy (MAE = 65.6 ± 21.0 s). This result suggests that researchers may be able to "calibrate" Graphormer-RT for more complex or uncommon chromatographic methods if examples are incorporated into training libraries.