In high-performance liquid chromatography, quantitative structure-retention relationships (QSRRs) are applied to model the relation between chromatographic retention and quantities derived from molecular structure of analytes. Classically a substantial number of test analytes is used to build QSRR models. This makes their application laborious and time consuming. In this work a strategy is presented to build QSRR models based on selected reduced calibration sets. The analytes in the reduced calibration sets are selected from larger sets of analytes by applying the algorithm of Kennard and Stone on the molecular descriptors used in the QSRR concerned. The strategy was applied on three QSRR models of different complexity, relating logk(w) or logk with either: (i) logP, the n-octanol-water partition coefficient, (ii) calculated quantum chemical indices (QCI), or (iii) descriptors from the linear solvation energy relationship (LSER). Models were developed and validated for 76 reversed-phase high-performance liquid chromatography systems. From the results we can conclude that it is possible to develop logP models suitable for the future prediction of retentions with as few as seven analytes. For the QCI and LSER models we derived the rule that three selected analytes per descriptor are sufficient. Both the dependent variable space, formed by the retention values, and the independent variable space, formed by the descriptors, are covered well by the reduced calibration sets. Finally guidelines to construct small calibration sets are formulated.