In traditional ML hyperparameter optimization the data is split up into train and test partitions via random split or cross-validation methods to further conduct hyperparameter tuning. In this study we aim to optimize the hyperparameters of the models specifically for the target task -active ML driven closed-loop optimization. This approach was applied since the hyperparameters need to be tuned according to the same low-data regime which will be required for subsequent applications within pH adjustment -using larger datasets and conducting train/test splits would not be relevant as this strategy differs from the target task.