Is Retrieval All You Need? Assessment and Emergence of Novelty in Protein Structure Generation

Protein backbone generation models are often credited with exploring novel fold space based solely on low full-chain similarity to known proteins, yet this cannot distinguish a genuinely new fold from a novel assembly of known structural units. To quantify the extent of this recombination, we introduce the Domain Retrieval Rate (DRR), defined as the fraction of generated backbones for which any constituent domain matches a known domain in CATH S40. Applied to eight backbone generation models spanning diffusion and flow-matching paradigms, DRR reveals that the vast majority of outputs contain domains already present in known fold space, even when their full-chain structures appear novel. The gap between domain-level and full-chain retrievability provides a direct measure of how much apparent novelty is combinatorial rather than structural. To calibrate what retrieval alone can achieve, we propose RetFold, a zero-training baseline that constructs backbones by retrieving CATH domains and refining inter-domain connections through geometry-based helix-linker optimization. RetFold attains designability and full-chain novelty on par with the strongest generators while running over two orders of magnitude faster on CPU alone. By saturating these metrics without learning, RetFold shows they cannot distinguish learned generation from retrieval and recombination. Together, DRR and RetFold establish that full-chain novelty is insufficient evidence of fold-level innovation, and future evaluation must decompose novelty across scales and calibrate against retrieval-based lower bounds.