Computational metrics are widely used to prioritize enzyme designs for experimental testing, yet their ability to capture catalytic function remains poorly established. Here, we systematically evaluate 33 in-silico metrics across four complementary tasks: distinguishing active from inactive enzymes, identifying substrate specificity, ranking enzyme variants by activity, and detecting perturbations to catalytic residues. We assess these capabilities using natural enzymes, computationally designed enzymes, activity-altering variants, and controlled structural perturbations that compare catalytic residues with matched non-catalytic sites. No single metric consistently captures all aspects of catalytic function. In activity discrimination, catalytic-residue intra-residue repulsion achieves the highest AUROC among the evaluated metrics for both natural enzymes (0.61) and 192 designs that passed the filters of their original design campaign (0.80). For substrate discrimination, interface predicted TM-score (ipTM) performs best (AUROC 0.70), whereas the catalytic pKa range shows the strongest association with activity across single-point variants of BglB (Spearman's rho = 0.48). In controlled perturbation tests, the minimum per-chain predicted TM-score (pTM) has a mean probability of 0.75 of responding more strongly to perturbations at catalytic residues than to matched perturbations at non-catalytic sites. These results reveal substantial variation in what current computational metrics capture, underscoring the need to distinguish structural confidence and physical plausibility from direct evidence of catalytic function when prioritizing enzyme designs.

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