The use of hybrid intelligent systems in industrial and commercial applications is briefly reviewed. The potential for application of such systems, in particular those that combine results from several constituent classifiers, to problems in drug design is discussed. It is shown that, although there are no general rules as to how a number of classifiers should best be combined, effective combinations can automatically be generated by genetic programming (GP). A robust performance measure based on the area under classifier receiver-operating-characteristic (ROC) curves is used as a fitness measure in order to facilitate evolution of multi-classifier systems that outperform their constituent individual classifiers. The approach is illustrated by application to publicly available Landsat data and to pharmaceutical data of the kind used in one stage of the drug design process.