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The Royal Society of Chemistry has hosted the ChemSpider database and associated platforms for over five years. Technologies made significant progress over that period but, more importantly, the community needs in terms of the variety of data types as well as search performance have increased. The preprocessing of chemicals for improved similarity searching and compound database navigation is seen as one crucial component of major development efforts to architect a new data repository. This component is engineered and implemented in collaboration with the group of Professor Oliver Kohlbacher at University of Tübingen. They have developed an approach for clustering large chemical libraries based on a fast, parallel, and purely CPU-based algorithm for 2D binary fingerprint similarity calculation. Using this method, the complete similarity network of our seed set with tens of millions of chemicals has been analyzed at a Tanimoto threshold of 0.6 and all similarity links were fed into our database. The latter is highly beneficial and will allow us to create more complex and enriching visualizations of similar compounds with associated bioactivity data and physicochemical properties for the RSC chemical repository users. This presentation will provide an overview of our experiences in applying clustering to our compound data and how it will be used to enrich data navigation on the RSC data repository.