I would respond in three interconnected points:

First, in a NVE simulation, the system explores a constant energy hypersurface, where all sampled microstates at that energy are indeed equally probable (assuming ergodicity). Theoretically, if the simulation runs indefinitely, it could sample all such microstates. Practically, the chaotic nature of molecular systems and numerical instabilities ultimately lead to slight energy drifts.

Second, in many lab or simulation experiments (e.g., those tracking chemical reactions, phase transitions, or protein conformational changes), the focus is typically on events occurring under conditions such as NPT or NVT, where the system exchanges energy with its surroundings, allowing it to explore different energy states. Under these conditions, the equiprobability of microstates no longer applies. Instead, the probability of each microstate decreases exponentially with its associated energy*. Consequently, higher energy states are exponentially less probable.

Finally, on a rugged potential energy surface, states of interest are often separated by high energy barriers, making transitions between such states rare and unlikely to occur within practical simulation times. It's to address this that a plethora of enhanced sampling methods (incl. metadynamics and umbrella sampling) have been developed. While their approaches vary, they all share the goal of improving sampling efficiency and enabling the observation of rare events within reasonable simulation time.

*proportional to exp(−E/kB​T) in NVT, and exp[−(E+pV)/kB​T] in NPT.