A scatterplot displays a relation between a pair of variables. Given a set of nu variables, there are nu(nu-1)/2 pairs of variables, and thus the same number of possible pairwise scatterplots. Therefore for even small sets of variables, the number of scatterplots can be large. Scatterplot matrices (SPLOMs) can easily run out of pixels when presenting high-dimensional data. We introduce a theoretical method and a testbed for assessing whether our method can be used to guide interactive exploration of high-dimensional data. The method is based on nine characterizations of the 2D distributions of orthogonal pairwise projections on a set of points in multidimensional Euclidean space. Working directly with these characterizations, we can locate anomalies for further analysis or search for similar distributions in a "large" SPLOM with more than a hundred dimensions. Our testbed, ScagExplorer, is developed in order to evaluate the feasibility of handling huge collections of scatterplots.