MHC class I's size-dependent peptide binding profiles could explain difficulties seen in recommending new peptides of different lengths with better pIC50s (Fig. ). For instance, the optimal peptide sequences significantly differ across 9-(FLMDWIWYV), 10-(FLMDFFLWYV), and 11-mers (KMIAMVLLIYV) according to the SMM PMBEC PSSMs. However, it is worth mentioning that MHC class I may be a special case, as anchoring residues are located at the N and C termini. Typically, central interactions are conserved, and modifications are made on peptide ends to enhance affinity or specificity . In such scenarios, the method may prove to be more effective. Interestingly, initiating BO with a diverse set of peptide lengths does not hinder performance, when the MAP4 fingerprint is coupled with either the homolog scanning or a structure-based method (Rosetta Flex ∆∆G); we observe a consistent improvement in pIC50s. (Fig. ). However, when coupled with the pretrained ESM-1b pLM and fine-tuned using the regular fine-tuning strategy, the results show that the initial batch composed of various peptide lengths shifts towards batches composed exclusively of 11-mers peptides. This results in BO plateauing after the second DMT cycle, unable to suggest other peptide lengths. On the contrary, the population of 9-, 10-, and 11-mers remained somewhat stable using the MAP4 fingerprint, with 8-mers peptides consistently down-prioritized. More importantly, BO initialized with predictions from the structure-based method was actually proved to be beneficial in that scenario, with 9-mers prioritized over 10-and 11-mers. Initialized with the homolog scanning strategy, 11-mers were shown to be prioritized instead, likely due to their better pIC50s in the initial batch, which persists despite the discovery of better-performing 9and 10-mers (Fig. ).