We introduce a novel approach to virus identification in transmission electron microscopy images using W1H-SPQ spectral binary hashing. By leveraging visual embeddings, we uncover a previously unreported phenomenon we call topological hysteresis: when virus classes are morphologically similar, the spectral structure of the graph becomes biased, limiting the model’s ability to distinguish within classes. To address this, we propose intra-class centering, a simple yet effective correction that stabilizes representations and restores discriminative power in the most affected cases. Combined with a hybrid refinement strategy, this approach achieves a strong balance between accuracy, compactness, and computational efficiency. The resulting system is open-set, hardware-agnostic, and lightweight, capable of running on resource-constrained devices without specialized hardware. This makes it especially relevant for real-world clinical deployment, including outbreak response, field hospitals, and low-resource settings. Beyond performance, this work contributes conceptually by identifying topological hysteresis and introducing a practical correction, opening new directions for topology-aware visual retrieval systems.