Insects navigate complex environments using compact visual systems and limited neural resources. Classic snapshot-based models of visual-homing assume panoramic, uniform-resolution input–an idealization that overlooks the anisotropic structure of real insect eyes. Here, we evaluate whether such models can operate under biologically realistic visual constraints. Using a 3D reconstruction of the British countryside and an accurate model of worker honeybees’ retina, we test the performance of a classic Perfect-Memory (PM) model and two new variants: Sampling PM (SPM) and Gated SPM (GSPM) on a visual-homing task faced by real bees. These variants store multiple views per location, each tagged with a goal direction, enabling direct retrieval of the goal azimuth with a single view, and aggregation of multiple independent predictions into a more robust decision. We find that PM is impaired by azimuthal anisotropy such as a blind-spot, while sampling-based models tolerate and even benefit from these constraints. Moreover, SPM and GSPM offer an estimate of certainty based on the variability and disagreement among multiple predictions. Finally, GSPM offers increased accuracy through allocentric gating, retrieving only views stored in the same global direction as the agent’s current heading. This limits memory access to a relevant subset, reducing aliasing and improving efficiency.