In this paper, the displacer concentration required to displace 50% of proteins bound in batch adsorption systems, DC-50, was employed as a means of ranking high-affinity, low molecular weight displacers for ion-exchange systems. A relatively large data set of cationic displacers with varying chemistries were evaluated with two proteins on two strong cation exchange resins in parallel batch screening experiments. Using this methodology, a significant number of high affinity displacers were identified that could displace both proteins at relatively low concentrations. In addition, the DC-50 data was used in concert with molecular structural information of the displacers to produce predictive quantitative structure-efficacy relationship (QSER) models based on a support vector machine (SVM) regression approach. The resulting models were well correlated and the predictive power of these models was demonstrated. Examination of the features selected in these models provided insight into the factors influencing displacer efficacy in cation exchange systems. These results demonstrate the utility of a combined DC-50/QSER approach to identify and design high-affinity displacers for ion-exchange displacement chromatography.