S4MPLE is a conformational sampling tool, based on a hybrid genetic algorithm, simulating one (conformer enumeration) or more molecules (docking). Energy calculations are based on the AMBER force field [Cornell et al. J. Am. Chem. Soc. 1995, 117, 5179.] for biological macromolecules and its generalized version GAFF [Wang et al. J. Comput. Chem. 2004 , 25, 1157.] for ligands. This paper describes more advanced, specific applications of S4MPLE to problems more complex than classical redocking of drug-like compounds [Hoffer et al. J. Mol. Graphics Modell. 2012, submitted for publication.]. Here, simultaneous docking of multiple entities is addressed in two different important contexts. First, simultaneous docking of two fragment-like ligands was attempted, as such ternary complexes are the basis of fragment-based drug design by linkage of the independent binders. As a preliminary, the capacity of S4MPLE to dock fragment-like compounds has been assessed, since this class of small probes used in fragment-based drug design covers a different chemical space than drug-like molecules. Herein reported success rates from fragments redocking are as good as classical benchmarking results on drug-like compounds (Astex Diverse Set [Hartshorn et al. J. Med. Chem. 2007, 50, 726.]). Then, S4MPLE is successfully challenged to predict locations of fragments involved in ternary complexes by means of multientity docking. Second, the key problem of predicting water-mediated interaction is addressed by considering explicit water molecules as additional entities to be docked in the presence of the "main" ligand. Blind prediction of solvent molecule positions, reproducing relevant ligand-water-site mediated interactions, is achieved in 76% cases over saved poses. S4MPLE was also successful to predict crystallographic water displacement by a therefore tailored functional group in the optimized ligand. However, water localization is an extremely delicate issue in terms of weighing of electrostatic and desolvation terms and also introduces a significant increase of required sampling efforts. Yet, the herein reported results - not making use of massively parallel deployment of the software - are very encouraging.