The acquisition functions plays a crucial role in selecting the location of the next set of observations, i.e., peptide sequences, based on a given set of previous observations. This function relies on the posterior distribution generated by the surrogate model, here the GPR model. Rather than directly optimizing the surrogate model and selecting which peptide sequences to test next based on their predicted means, the surrogate model is optimized through the acquisition function. The choice of the peptide sequences to test is also based on the uncertainty associated with these predictions. Therefore, the acquisition function takes into account both to make the best possible selection of peptide sequences for testing. In this work, we tested the following acquisition functions: probability of improvement (PI) , the expected improvement (EI) , the upper confidence bound (UCB) and a simple greedy approach based on the posterior mean from the GPR model.