Adaptive sampling In adaptive sampling, simulations are continuously monitored, and promising states are reseeded regularly to encourage better exploration. This method allows you to strike a balance between revisiting known states (exploitation) and discovering new states (exploration) (Fig. ). By dynamically adjusting the sampling process, one can avoid oversampling common states and under-sampling rare transitions, ensuring a more thorough exploration of the system while observing multiple important transitions. This adaptive approach is especially useful when the goal is to achieve both new state discovery and robust sampling of transitions. Correspondingly, adaptive sampling is limited by its inherently depends on the quality of the initial sampling and on the goal function. If early simulations fail to discover critical states, the algorithm may bias itself towards suboptimal regions of the conformational space, potentially missing significant transitions or important rare states. Additionally, if the goal function that is being used to seed states is imprecise, suboptimal regions of conformational space may be ranked highly, leading to redundant sampling. Furthermore, one should take care from directly analyzing adaptive sampling simulations for state populations and conformational transition; the inherent bias of reseeding simulations regularly from ranked states will induce a bias in the conformational landscape samples, which can lead to "tunneling" like effects . Adaptive seeding Another approach is adaptive seeding, where specific states along a pathway-such as those on the minimum free energy pathway-are selected as starting points for launching multiple parallel simulations . By ensuring there is overlap between different trajectories, this method can provide a more comprehensive and well-sampled dataset. This strategy allows for parallel exploration of the conformational landscape while maintaining the ability to observe important transitions and collect robust thermodynamic and kinetic predictions . Adaptive seeding is however, heavily dependent on the seeds describing an appropriate pathway or configurational space. The initial seeds should represent the most functionally relevant regions of the conformational space to ensure that simulations capture the the full ensemble of pathways within the system's functional landscape. Proper seeding pathways will capture an ensemble of pathways that couple large biochemical transitions and long-timescale events. To ensure that analysis of seeded simulations is not biased by the seed distributions, one should ensure an equal amount of simulation time is collected per seed structure. Lastly, it is useful to check for the degree of state overlap between trajectories from different seeds, and collect more data if such conformational mixing is poor . When adaptively-seeded datasets are appropriately sampled they provide powerful datasets for capturing incredibly long-timescale events and provide insight into fundamental biochemical processes .