Hydrogen-evolution activity is commonly rationalized through hydrogen adsorption energies and the Sabatier principle, yet this descriptor picture becomes ambiguous on multimetallic surfaces, where each composition exposes a distribution of local adsorption environments. Here we investigate whether the bare-surface work function, previously shown to add predictive information for monometallic surfaces, remains an activity descriptor for compositionally complex surfaces. We test this on three combinatorial Ag-Au-Pd-Pt thin-film materials libraries screened for acidic hydrogen evolution by scanning electrochemical cell microscopy. Graph neural networks provide adsorption-energy distributions and work functions for each measured composition. A work-function-only model explained most of the activity variation (mean R²log = 0.903), as did a coverage-corrected adsorption model (mean R²log = 0.955), outperforming dilute adsorption (mean R²log = 0.758). Combining work function and coverage-corrected adsorption yielded the highest fit quality (mean R²log = 0.969), but only a small gain over coverage-corrected adsorption alone. For these four metals the coverage-corrected adsorption energy and work function follow a similar trend, producing similar activity rankings, hence including both adds little beyond either one individually, although both are strong predictors.