With the continuous progress in ultra-large virtual libraries which are readily accessible, it is of great interest to explore this large chemical space for hit identification and lead optimization using reliable structure-based approaches. In this work, a novel growth-based screening protocol has been designed and implemented in the structure-based design platform CONTOUR®. The protocol was used to screen the ZINC database in silico and optimize hits to discover 11β-HSD1 inhibitors. In contrast to molecular docking, the virtual screening process makes significant improvements in computational efficiency without losing chemical equities through partitioning 1.8 million ZINC compounds into fragments, docking fragments to form key hydrogen bonds with anchor residues, reorganizing molecules into molecular fragment trees using matched fragments and common substructures, and then regrowing molecules with the help of developed intelligent growth features inside the protein binding site to find hits. The growth-base screening approach is validated by the high hit rate. Total 50 compounds have been selected for testing, of these, 15 hits having diverse scaffolds are found to inhibit 11β-HSD1 with IC50 values of less than 1M in a biochemical enzyme assay. The best hit which exhibits an enzyme IC50 of 33nM is further developed to a novel series of bicyclic 11β-HSD1 inhibitors with the best inhibition of enzyme IC50 of 3.1nM. The final lead candidate exhibits IC50s of 7.2nM and 21nM in enzyme and adipocyte assays, respectively, displayed greater than 1000-fold of selectivity over 11β-HSD2 and two other related hydroxysteroid dehydrogenases, and can serve as good starting points for further optimization to develop clinical candidates.