Applications of BO on peptides and polymers in semi-automatic contexts have recently been published. explored the use of a Naive Bayes classifier for de novo design of peptide substrates , while Hughes et al. employed a combination of Gaussian Process Regressor (GPR) model and a Genetic Algorithm (GA) to optimize peptide sequences to bind gold and silver metal ions . Other recent studies have used BO to optimize co-polymers for stabilizing enzymes , polymers that can bind organic molecules , or design new antimicrobial peptides containing non-proteinogenic amino acids . Hybrid strategies combining several rounds of optimization using all-atom Molecular Dynamics (MD) simulations with experimental assays has also been employed for the design of self-assembling π-conjugated peptides . These studies provide experimentally validated demonstrations that BO can be applied to peptide optimization tasks. However, to date, limited research has been conducted on the systematic benchmarking of BO in the context of peptide optimization. The influence of different parameters, conditions, and initialization strategies (structure-or sequence-based methods) of the DMT optimization process on their performance has yet to be thoroughly explored. A comprehensive and systematic evaluation of these methods is essential to understand their strengths and weaknesses, as well as potential improvements and best practices for their applications in peptide optimization tasks. Alongside the systematic evaluation of BO (Fig. ), developing more generalized approaches that allow for the incorporation of non-standard amino acids and the simultaneous exploration of different peptide scaffolds and lengths represents a necessary step towards the design of bioactive peptides with finely tuned properties.