The computational construction of small organic molecules (de novo design), directly in a protein binding site, is an effective means for generating novel ligands tailored to fit the pocket environment. In this work, we present two new methods, which aim to improve de novo design outcomes using (1) biasing algorithms to prioritize selection and/or acceptance of fragments and torsions during growth, and (2) parallel-based clustering and pruning algorithms to remove duplicate molecules as candidate fragment are added. Large-scale testing encompassing thousands of simulations were employed to interrogate the methods in terms of multiple metrics which include numbers of duplicate molecules generated, pairwise-similarity, focused library reconstruction rates, fragment and torsion frequencies, fragment and torsion rank scores, interaction energy and drug-likeness scores, and 3D pose comparisons. The biasing algorithms, particularly those that include fragment and torsion components simultaneously, led to molecules that more closely mimicked the distributions of fragments and torsions found in drug-like libraries. The new parallel-based clustering and pruning algorithms, compared with the existing serial approach, also led to larger ensembles comprised of topologically unique molecules with much greater efficiency by removing redundant growth paths.