Existing classical force fields are sometimes insufficient to estimate specific properties of interest, such as polarization and charge delocalization effects for complex biological systems. Timesteps for MD simulations are restricted within the femtosecond range to correctly integrate the equations of motion as they cannot exceed the time period of highestfrequency thermal oscillation. Biologically relevant systems undergo complex conformational transitions, which are essential to their functions. It is challenging to capture these transitions through conventional MD, especially if they are relatively slow processes (milliseconds or longer), involve large-scale conformational rearrangements, or include protein association and/or (re)folding. We resort to computationally expensive long-scale MD simulations to observe such transitions or "rare events". Several enhanced sampling methods have been developed to overcome the difference in timescales of conventional MD simulations compared to the timescales of biological processes. One category of enhanced sampling methods adds a bias potential to the potential energy surface (PES) that decreases the energy barrier of transition between metastable states and accelerates the conformational search. We could roughly categorize these methods as collective variable (CV)-based and CV-free enhanced sampling methods. CV-based enhanced sampling approaches include and are not limited to metadynamics (metaD), variationally enhanced sampling, and Markov state models (MSMs) while CV-free enhanced sampling methods include parallel tempering or replica exchange molecular dynamics (REMD), selective integrated tempering, and Gaussian accelerated molecular dynamics (GaMD). All the previously mentioned enhanced sampling approaches are capable of accelerated PES conformational search. Thereby, they prove to be effective in extensive thermodynamic sampling. On the contrary, such enhanced sampling approaches add a bias potential to the system's potential energy and thereby lead to altered dynamics.