Alongside our earlier findings, we also investigated the impact of sequence-based initialization strategies and evaluated the performance of our method when considering multiple MHC class I alleles simultaneously. In multi-objective optimization, it's not always possible for a single solution, such as a peptide sequence, to optimize all objectives, like pIC50s for different MHC class I alleles, simultaneously. This often results in trade-offs, where optimizing for one allele might worsen the pIC50 for another. To put ourselves in such a situation, we selected two MHC class I alleles that bind peptides with different sequence profiles, MHC class I A*0101 and A*2602. The peptide sequence with the best pIC50 for A2602 is YTDMYTLYY (pIC50 of 0.08), while for A0101, it is DVIRHMGAM (pIC50 of -1.36). Optimizing against one allele adversely affects the pIC50 for the other due to the differing peptide sequence characteristics each allele prefers. The results were consistent with those observed with a single allele (Fig. ). Both the MAP4 fingerprint and the top-performing pLM (fine-tuned ESM-1b) outperformed a random search, with ESM-1b outperforming the MAP4 fingerprint. With ESM-1b, the lowest theoretical pIC50 is reached in 2 DMT cycles for allele MHC class I A*2602, and within one log unit for A*0101. However, when comparing the Pareto fronts, both perform similarly (Fig. ). The hypervolume is approximately 27.38 for ESM-1b and 26.98 for the MAP4 fingerprint, compared to 15.44 for a random search. Considering the number of nondominated solutions found across all the independent BO runs, 64 and 62 are found in total for ESM-1b and the MAP4 fingerprint, respectively, significantly higher than the 6 nondominated solutions for the random search.