Fundus images from the two eyes of one patient can share disease and acquisition cues, making matching performance difficult to interpret. We developed a controlled evaluation protocol for same-patient cross-eye matching that combines disease matching, image quality controls, and patient-level bootstrap resampling. Frozen features from an ImageNet-pretrained 50-layer residual network (ResNet-50) were evaluated in 300 patients from DeepDRiD, a retinal dataset with two photographs per eye and patient-disjoint splits. In a post hoc 34-patient test subset matched on ordered bilateral diabetic retinopathy grades and right-eye quality tier, the area under the receiver operating characteristic curve (AUROC) was 0.861 (interval envelope, 0.681–0.995). A separate 33-patient four-view analysis showed higher cross-eye than matched-negative similarity; the interval for the same-eye–cross-eye difference included zero. External validation in 244 patients from the independent PAPILA glaucoma dataset yielded AUROC 0.859 under bilateral diagnosis matching (interval envelope, 0.804–0.907). A learned hierarchical head achieved a higher validation AUROC point estimate than frozen features in DeepDRiD (0.882 versus 0.843) but a lower estimate in a previously inspected 137-patient fundus–optical coherence tomography cohort (0.849 versus 0.902). These results support cross-eye correspondence under the tested controls and show that source-cohort optimization does not ensure better transfer. The protocol provides a reproducible assessment of bilateral fundus representations across cohorts.