Solvated macromolecules such as polymers and proteins undergo conformational transitions in response to diverse environmental stimuli such as pressure, temperature, pH, ligand binding, and post-translational modification. These transitions are fundamental to biological function, from cellular signaling to misfolding and aggregation, and are increasingly harnessed in applications such as drug delivery, biosensing, and biomaterials design. Understanding how such stimuli shape macromolecular conformation at atomistic resolution is critical, but experimentally challenging. Molecular simulations offer a powerful route to study these transitions in atomistic detail, yet conventional approaches often struggle to capture them, as the relevant timescales exceed those accessible to standard simulations. In this work, we develop enhanced sampling strategies to overcome these limitations, focusing on a small but challenging subset of systems and stimuli. We begin by examining polymer collapse, a model system for solvent-driven transitions, and evaluate the performance of various chain and solvent-based collective variables (CVs) for sampling its free energy landscape. Our results highlight how sampling is hindered at longer chain lengths, suggesting the presence of orthogonal barriers, and reveal general limitations with the CVs commonly used to study polymer collapse. Next, we turn to proteins, and explore the impact of a simple stimulus on them: hydrostatic pressure. Proteins are known to denature under elevated pressures in the kilobar range. Studying their pressure response can shed light on the molecular basis of extremophilic adaptation and help uncover latent functional sites, particularly those exposed upon allosteric activation. However, simulating pressure-induced responses remains difficult due to slow, solvent-coupled kinetics. We introduce a hydration-based biasing strategy that mimics the thermodynamic effects of pressure while bypassing its kinetic bottlenecks, and benchmark our simulation predictions against high-pressure experimental data. We demonstrate the application of this approach in identifying latent conformationally-flexible protein interfaces. We also use this method to map pressure-temperature stability landscapes of homologs from mesophilic and thermophilic organisms. Together, this work presents a computational framework for studying conformational responses to environmental stimuli, enabling mechanistic insight across proteins, polymers, and other solvated macromolecules.