In all previous benchmarking tasks, we evaluated the performance of BO in ideal conditions, e.g. in absence of noise. Yet, real-world experimental data often contains noise, making the study of BO under these degraded conditions more relevant. This approach not only provides a more realistic evaluation but also helps in anticipating and managing unexpected outcomes in a DMT campaign. Additionally, we investigated the effect of smaller batch sizes, as it is not always possible to test large batches of 96 peptides at each DMT cycle, due to budget and human constraints. We also examined different batch strategies, KB and CL are the most popular heuristic strategies due to their simplicity , and compared against a simple top-k batch strategy, alongside various acquisition functions like EI, greedy, PI, and UCB. Results confirm that BO is noise-sensitive, impacting performance significantly (Fig. and S14), in line with previous findings that performance declines with high noise levels, specifically above 1 σ . As noise level rises, the incremental improvement in pIC50s across DMT cycles lessens. When noise reaches a sigma level of 1, BO's results begin to stray markedly from the noise-free reference curve, by over one log unit. On contrary, BO handles smaller batches more effectively than noise (Fig. and). For instance, with a batch size of 48 peptides, BO stays close to the reference curve of 96 peptides. A batch of 72 peptides shows unaffected performance, while a batch of 12 peptides requires more than 3 DMT cycles to outperform a random search.