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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, 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, molecules or to  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} Alignment methods attempt to either maximize the volume overlap of two molecules or the correspondence between identified feature points, such as molecular field extrema\cite{Vainio2009,Cheeseright2006}. Volume overlap is usually maximized by representing the molecular shape as a collection of Gaussians,\cite{Good1993,Grant1996} sampling several starting points, and using numerical optimization to find a local maximum. Alternative, the molecule may be decomposed into a set of features, such as pharmacophore features\cite{Sastry2011}, field points\cite{Thorner1996,Vainio2009,Cheeseright2006}, or hyperbolical paraboloid representations of patches of molecular surface\cite{Proschak2008}, and various point correspondence algorithms may be used to generate an alignment. Although a number of performance improvements to alignment methods have been described,\cite{Grant1996,RushIII2005,Sastry2011,Fontaine2007} the task remains computationally intensive.