Drug combination models must represent cellular context in a form that remains informative when the tested cells or compounds differ from those used for training. Transcription factor regulons offer a biologically structured representation, but their contribution can depend on the accompanying features and the intended prediction task. We developed a symmetric three-class predictor of DrugComb ZIP interactions and compared all seven combinations of landmark expression, Hallmark pathway scores and CollecTRI regulon activities. The common dataset contained 302,042 observations across 3,042 drugs and 155 cellular contexts. We evaluated three group-held-out generalization settings: unseen cell lines, unseen drug scaffolds, and simultaneous exclusion of both, with five training seeds per representation and partition. Pathways plus regulons achieved the highest mean Macro-F1 on unseen cell lines (0.4737, compared with 0.4503 for expression alone). Compared with pathways alone, adding regulons increased mean Macro-F1 by 0.0153 on unseen cell lines and 0.0229 on unseen drug scaffolds, with positive differences across all five paired seeds in both settings. In contrast, the same addition reduced the mean under joint context and scaffold exclusion. Representation rankings also depended on the objective: regulons alone achieved the highest synergy average precision on unseen lines, whereas the Macro-F1-leading combination did not maximize precision among the top-ranked candidates. Further compression of pathways into non-negative matrix factorization programs did not exceed the best context-model Macro-F1 in any setting. These results demonstrate that the predictive value of functional context representations depends on both the type of domain shift and the evaluation objective, and that combining additional biological representations does not necessarily improve generalization. They provide a computational basis for studying drug combinations in senescent states, where transfer must subsequently be assessed using state-specific responses and matched controls.