Surprisingly, language models trained exclusively on protein sequences, such as ESM-1b and ProtT5, outperform the MAP4 fingerprint in SOO and MOO when fine-tuned on the peptide sequences. The regular fine-tuning method, applied only to the last self-attention layer, shows superior performance than LoRA fine-tuning applied to all the self-attention layers. However, when fine-tuned using peptides of different lengths (8-to 11-mers), BO shows poor performance as the pLMs (ESM1-b) seem unable to deal with variability in the sequence length. In comparison, the MAP4 fingerprint method appears to be a strong baseline for peptide optimization tasks.