Abstract Motivation : The RNA-Recognition motif (RRM) is a protein domain that binds single-stranded RNA (ssRNA) and is present in as much as 2% of the human genome. Despite this important role in biology, RRM-ssRNA interactions are very challenging to study on the structural level because of the remarkable flexibility of ssRNA. In the absence of atomic-level experimental data, the only method able to predict the 3D structure of protein-ssRNA complexes with any degree of accuracy is ssRNA’TTRACT, an ssRNA fragment-based docking approach using ATTRACT. However, this approach has limitations, such as the production of only a handful of near-native poses amid many non-natives, and the frequent failure of the ATTRACT scoring function (ASF) to recognize these near-natives. Nevertheless, since ASF parameters are not ssRNA-specific and were determined in 2010, there is substantial opportunity for enhancement. Results Here we present HIPPO, a composite RRM-ssRNA scoring potential derived analytically from contact frequencies in near-native versus non-native docking models. Validated on a fragment-based docking benchmark of 57 experimentally solved RRM-ssRNA complexes, HIPPO achieved a 3-fold or higher enrichment for half of the fragments, versus only a quarter with ASF. In particular, HIPPO drastically improved the chance of very high enrichment (12-fold or higher), a scenario where the incremental modelling of entire ssRNA chains from fragments becomes viable. However, for the latter result, more research is needed to make it directly practically applicable. Regardless, our approach already improves upon the state of the art in RRM-ssRNA modelling and is in principle extendable to other types of protein-nucleic acid interactions.