High-throughput screening, based on subcellular imaging, has become a powerful tool in lead discovery. Through the generation of high-quality images, not only the specific target signal can be analyzed but also phenotypic changes of the whole cell are recorded. Yet analysis strategies for the exploration of high-content screening results, in a manner that is independent from predefined control phenotypes, are largely missing. The approach presented here is based on a well-established modeling technique, self-organizing maps (SOMs), which uses multiparametric results to group treatments that create similar morphological effects. This report describes a novel visualization of the SOM clustering by using an image of the cells from each node, with the most representative cell highlighted to deploy the phenotype described by each node. The approach has the potential to identify both expected hits and novel cellular phenotypes. Moreover, different chemotypes, which cause the same phenotypic effects, are identified, thus facilitating "scaffold hopping."