Abstract: Image profiling, transcriptomics, proteomics and metabolomics are a group of ‘omics tools that can provide phenotypic fingerprints in cellular systems to boost drug discovery programs stemming from genetically validated targets. In particular, phenotypic data from ‘omics tools aim to provide early mechanism of action (MoA) prediction, expansion of the druggable space, and improved prospects for first-in-class medicine discovery, which has historically benefitted from phenotypic approaches. Yet numerous challenges exist in practical implementation of ‘omics platforms to provide a results database that is suitable in drug discovery, including: standardisation of data generation, selection of quality control constraints, and development of methods to analyse ‘omics data views in a shared embedded space. We describe how these practical challenges have been addressed in the development of a high-content profiling platform that uses Cell Painting (image profiling) and DRUG-seq (transcriptomics) at GSK. Within the platform, the data analysis component is targeted using a purpose-built framework called Phenonaut, an open-source Python package for multiomics built for use by non-computational specialists. A case study on application of the high-content profiling platform to a small molecule program is presented. Results from image and transcriptomic profiling of a group of 160 lead molecules triaged from a target-centric screening strategy identified two compounds, which would otherwise have been down-prioritised. The two compounds identified represent distinct molecular starting points to tackle target-associated disease, demonstrate a lack of direct binding to the target, exhibit morphological profiles that group with both literature activators and pathway modulators of the target, demonstrate transcriptomic on-pathway effects, and show enhancement target activity in an orthogonal cellular assay. Furthermore, in mapping morphological profiles against mechanistic controls the MoA of the compound group was found to associate with IGF-1 inhibition, which is the subject of further investigation as a potential repurposing route. This case study provides an example of how GSK is incorporating ‘omics technologies for modern phenotypic discovery to augment traditional drug discovery pipelines.