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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 toa  known active molecules or to a pseudo-ligand that is derived from the desired binding site\cite{Ebalunode2008}. Shape similarity is usually assessed either through alignment methods, which seek to maximize the three dimensional overlap of two shapes, or through feature vector methods, which transform shapes into a low-dimension vector of features that can be efficiently compared. As part of the similarity calculation, molecular shapes may be further annotated with electrostatic or pharmacophore features.\cite{Vainio2009,Cheeseright2006,Thorner1996,Tervo2005,Marin2008,Sastry2011} Alignment methods try to find the optimal overlay of two molecules to either maximize the overlapping volume or the correspondence between feature points, such as molecular field extrema\cite{Vainio2009,Cheeseright2006}. The predominant method of maximizing volume overlap is to represent the molecular shapes as a collection of Gaussians,\cite{Good1993,Grant1996} sample several starting points, and use numerical optimization to find a local maximum.\cite{Hawkins_2007} Instead of Gaussians, the molecular shape can be represented by features and point correspondence algorithms can be used to generate the alignment. Potential features include pharmacophore features\cite{Sastry2011}, field points\cite{Thorner1996,Vainio2009,Cheeseright2006}, or hyperbolical paraboloid representations of patches of molecular surface\cite{Proschak2008}. A number of performance optimizations to the alignment process have been described\cite{Grant1996,RushIII2005,Sastry2011,Fontaine2007}, but alignment methods remain computationally expensive relative to feature vector methods, unless shapes can be pre-aligned to a canonical coordinate system, as is done with Volumetric Aligned Molecular Shapes (VAMS).\cite{Koes_2014}