The identification of feasible operating conditions during the early stages of bioprocess development is implemented frequently through High Throughput (HT) studies. These typically employ techniques based on regression analysis, such as Design of Experiments. In this work, an alternative approach, based on a previously developed variant of the Simplex algorithm, is compared to the conventional regression-based method for three experimental systems involving polishing chromatography and protein refolding. This Simplex algorithm variant was found to be more effective in identifying superior operating conditions, and in fact it reached the global optimum in most cases involving multiple optima. By contrast, the regression-based method often failed to reach the global optimum, and in many cases reached poor operating conditions. The Simplex-based method is further shown to be robust in dealing with noisy experimental data, and requires fewer experiments than regression-based methods to reach favorable operating conditions. The Simplex-variant also lends itself to the use of HT analytical methods, when they are available, which can assist in avoiding analytical bottlenecks. It is suggested that this Simplex-variant is ideally suited to rapid optimization in early-phase process development.