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% introduction and background  Molecular shape is a useful component fundamental concept  in the identification of small-molecules for therapeutic intervention,\cite{Nicholls2010} medicinal chemistry \cite{Nicholls2010},  and shape-based virtual screens have successfully identified novel inhibitors.\cite{RushIII2005,McMasters2009,Muchmore2006,Naylor_2009} Shape is used both to assess the similarity of a candidate molecule to a set of known actives and to evaluate the complementarity of a molecular shape to the shape of the binding site on the target receptor. Here we describe a novel method for \textit{exactly} specifying shape constraints for querying a large library of molecular shapes. The shape constraints are derived from both the receptor, which describes where a molecule cannot be, and from the shape of a known binder, which describes where the molecule should be.  Our method is unique in relying on a manually positioned ligand \textit{anchor fragment}. This anchor fragment requirement makes our approach particularly applicable to a fragment-based drug discovery workflow\cite{Rees2004,Congreve2008} as shape constraints are a natural way to search for compounds that extend an identified fragment structure while remaining complementary to the receptor.  %Additionally, Additionally,  the anchor fragment, by defining a fixed coordinate system, enables the indexing of large libraries of molecular shapes. This indexing allows search times to scale sub-linearly with the size of the library, resulting in search performance that is on an interactive time scale. Shape-based virtual screening typically attempts to identify the most similar molecules in a virtual library to a given set of one or more known active molecules. Alternatively, if the receptor structure is available, a pseudo-ligand can be derived from the desired binding site\cite{Ebalunode2008}. Shape similarity can be determined either through alignment methods, which construct a three dimensional overlay of two shapes, or through feature vector methods, which reduce shapes to a lower-dimension vector of features that are compared numerically. In addition to steric volume, the electrostatic or pharmacophore features of the shape may be taken into account when assessing similarity.\cite{Vainio2009,Cheeseright2006,Thorner1996,Tervo2005,Marin2008,Sastry2011}