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Optimizing complex bioprocesses poses a significant challenge in several fields, particularly in cell therapy manufacturing. The development of customized, closed, and automated processes is crucial for their industrial translation and for addressing large patient populations at a sustainable price. Limited understanding of the underlying biological mechanisms, coupled with highly resource-intensive experimentation, are two contributing factors that make the development of these next-generation processes challenging. Bayesian optimization is an iterative experimental design methodology that addresses these challenges, but has not been extensively tested in situations that require parallel experimentation with significant experimental variability. In this study, we present an evaluation of noisy, parallel Bayesian optimization for increasing noise levels and parallel batch sizes on two <i>in silico</i> bioprocesses, and compare it to the industry state-of-the-art. As an <i>in vitro</i> showcase, we apply the method to the optimization of a monocyte purification unit operation. The <i>in silico</i> results show that Bayesian optimization significantly outperforms the state-of-the-art, requiring approximately 50% fewer experiments on average. This study highlights the potential of noisy, parallel Bayesian optimization as valuable tool for cell therapy process development and optimization.