High-content screening (HCS) is a powerful technique for monitoring phenotypic responses to treatments on a cellular and subcellular level. Cellular phenotypes can be characterized by multivariate image readouts such as shape, intensity, or texture. The corresponding feature vectors can thus be defined as HCS fingerprints that serve as a powerful biological compound descriptor. Therefore, clustering or classification of HCS fingerprints across compound treatments allows for the identification of similarities in protein targets or pathways. We developed an HCS-based profiling panel that serves as basis for characterizing the mode of action of compounds. This panel measures phenotypic effects in six different compartments of U-2OS cells, namely the nucleus, the cytoplasm, the endoplasmic reticulum, the Golgi apparatus, and the cytoskeleton. We profiled a set of 2,725 well-annotated compounds and clustered their corresponding HCS fingerprints to establish links between predominant cellular phenotypes and cellular processes and protein targets. We found various different clusters enriched for individual targets (e.g., HDAC, HSP90, TOP1, HMGCR, TUB), signaling pathways (e.g., PIK3/AKT/mTOR), or gene sets associated with diseases (e.g., psoriasis, leukemia). Based on this clustering we were able to identify novel compound-target associations for selected compounds such as a submicromolar inhibitory activity of Silmitasertib (a casein kinase inhibitor) on PI3K and mTOR.