The necessity to generate conformations that sample the entire conformational space accessible to a given molecule is ubiquitous in the field of computer-aided drug design. Protein-ligand docking, 3D database searching, and 3D QSAR are three commonly used techniques that depend critically upon the quality and diversity of the generated conformers. Although there are a wide range of conformational search algorithms available, the extent to which they sample conformational space is often unclear. To address this question, we conducted a robust comparison of the search algorithms implemented in several widely used molecular modeling packages, including Catalyst, Macromodel, Omega, MOE, and Rubicon as well as our own method, stochastic proximity embedding (SPE). We found that SPE used in conjunction with conformational boosting, a heuristic for biasing conformational search toward more extended or compact geometries, along with Catalyst, are significantly more effective in sampling the full range of conformational space compared to the other methods, which show distinct preferences for either more extended or more compact geometries.