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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 agiven set of one or more  known active molecules or to a pseudo-ligand can be that is  derived from the desired binding site\cite{Ebalunode2008}. Shape similarity can be determined is usually assessed  either through alignment methods, which construct a seek to maximize the  three dimensional overlay overlap  of two shapes, or through feature vector methods, which reduce transform  shapes to into  a lower-dimension low-dimension  vector of features that are compared numerically. In addition to steric volume, the electrostatic or pharmacophore features can be efficiently compared. As part  of the shape similarity calculation, molecular shapes  may be taken into account when assessing similarity.\cite{Vainio2009,Cheeseright2006,Thorner1996,Tervo2005,Marin2008,Sastry2011} further annotated with electrostatic or pharmacophore features.\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.