Modeling nucleic acids, particularly RNA, presents challenges due to their dynamic nature, with biologically relevant conformations often differing significantly between unbound and bound states. This variability complicates predictions in silico, making efficient conformational sampling crucial. In this presentation, we investigate the efficacy of the LowModeMD method for enhancing conformational sampling in DNA and RNA systems compared to traditional molecular dynamics approaches.By focusing on low-frequency vibrational modes, LowModeMD enables rapid exploration of conformational space, facilitating the identification of biologically relevant structures. We demonstrate the utility of LowModeMD in analyzing bulged residues, sampling terminal and internal loops, cryptic pockets and large conformational changes caused by complexation.