High-Throughput Screening (HTS) data in its entirety is a valuable raw material for the drug-discovery process. It provides the most compete information about the biological activity of a company's compounds. However, its quantity, complexity and heterogeneity require novel, sophisticated approaches in data analysis. At GeneData, we are developing methods for large-scale, synoptical mining of screening data in a five-step analysis: (1) Quality Assurance: Checking data for experimental artifacts and eliminating low quality data. (2) Biological Profiling: Clustering and ranking of compounds based on their biological activity, taking into account specific characteristics of HTS data. (3) Rule-based Classification: Applying user-defined rules to biological and chemical properties, and providing hypotheses on the biological mode-of-action of compounds. (4) Joint Biological-Chemical Analysis: Associating chemical compound data to HTS data, providing hypotheses for structure- activity relationships. (5) integration with Genomic and Gene Expression Data: Linking into other components of GeneData's bioinformatics platform, and assessing the compounds' modes-of-action, toxicity, and metabolic properties. These analyses address issues that are crucial for a correct interpretation and full exploitation of screening data. They lead to a sound rating of assays and compounds at an early state of the lead-finding process.