In this work, we investigated three systems to show two important features of the af2rave package, structural ensemble generation and expedited sampling. We showed our protocol can generate conformation ensembles for ADK and DDR1, and classify them into meaningful states. With the RBD of SARS-CoV-2 spike protein, we showed that good sampling on the conformational landscape can be achieved by significantly shorter sampling. Nonetheless, the scope of our method extends beyond these two applications. For example, the latent space produced by SPIB can be an effective way to project structures into lowerdimensional spaces for visualization, or used for further enhanced sampling to generate more structures. Additionally, these latent spaces are often transferable across proteins within the same class. In summary, the af2rave protocol enabled two critical outcomes: (1) initial conformation identification without simulation and efficient conformational sampling via strategically selected cluster centers. (2) the short simulations from wisely chosen staring points can gather enough information about the more global conformational landscape of interest. This "tunneling" strategy bypasses slow barrier-crossing events by initializing simulations in distinct basins, decoupling sampling efficiency from barrier heights. The results demonstrate that combining AI-derived structural hypotheses with adaptive MD initialization accelerates conformational landscape mapping, offering a generalizable framework for studying biomolecular systems where long-timescale sampling remains prohibitive.