Identifying novel cancer drug candidates requires a comprehensive understanding of drug-induced molecular changes. This study integrates metabolomics and proteomics data with computational methods to investigate the mechanisms of action (MOA) of previously uncharacterized compounds from a large-scale, high-throughput screening experiment. Our approach systematically compares internally generated proteomic profiles with an external dataset of 875 known small molecules, employing computational strategies to mitigate biases inherent in cross-dataset comparisons. Through this multi-omics approach, this study identifies promising cancer drug candidates by uncovering specific molecular targets, potential anticancer mechanisms, and similarities to known compounds with established anticancer effects. By integrating experimental data with computational analyses, this study not only advances the identification of potential cancer therapeutics but also provides a framework for integrating multi-omics data across diverse datasets.