Glycosaminoglycans (GAGs) are a diverse group of linear anionic periodic polysaccharides that participate in many biological processes through the regulation of their protein partners activity. They are produced by many cell types and are found in the extracellular space as well as on cell surfaces, where they play an important role in mediation of cell-extracellular matrix interactions. Crystallization of protein-GAG complexes is difficult, therefore molecular docking can be a useful technique for predicting the binding conformation and understanding specific interactions in protein-GAG systems. At the same time, GAGs are also very challenging ligands for docking due to their high flexibility, periodicity and charged nature. Previously, we tested six different molecular docking software in terms of the performance on the protein-GAG complexes. In this study, we further performed docking simulations with other eight open access docking programs (Dock, rDock, ClusPro, PLANTS, HADDOCK, Hex, SwissDock and ATTRACT) for a dataset of 28 protein-GAG complexes with experimentally available structures, where a GAG ligand was longer than a trimer. Our results showed that Dock yielded the best prediction of a GAG binding pose, and its performance was independent of a GAG length. Overall, although the ligand binding poses could be correctly predicted in many cases by the tested docking programs, the ranks of the docking poses are often poorly assigned. Further comparison of the performance of fourteen docking programs, eight of which were analyzed in this study and six in the previous one, with the binding free energy components calculated for the corresponding experimental complexes allowed us to establish which binding free energy patterns define the success of each of these docking programs. Our work contributes to the evaluation of computational tools that could be used specifically to decipher protein-GAG interactions.