The Inverse Folding problem involves identifying RNA sequences that adopt a target structure with respect to free-energy minimization, i.e. preferential to all alternative structures. The problem has historically been regarded as challenging, largely due to its proven NP-completeness of an extended version where the base pair maximization energy model is used. In contrast, it has recently been shown that a large subset called m-separable structures, notably including those comprising helices of length 3+, can be solved in linear-time within the same energy model. This permits not only the identification of a single solution, but also the characterization of a language of solutions. In this work, we seek to describe the ``hardness'' of Inverse Folding, bridging (at least heuristically) the gap between a simplified energy model and a more realistic Turner energy model. We used LinearBPDesign to generate seed sequences for RNAinverse, thereby improving the design process in a Turner energy model. To this end, we extended LinearBPDesign to accommodate biseparability and to handle non- or high modulo separable structures by minimalist addition of base pairs. Our study suggests that seeds generated by LinearBPDesign capture long-range interactions, thereby improving the performance of RNAinverse compared to seed focusing on refining the energy model itself. Most surprisingly, a significant number of LinearBPDesign seeds uniquely fold into the target structure in the Turner model, especially when helices are at least of length 2. This observation suggests that the ``hardness'' of design may arise from the intrinsic properties of the structures themselves.