Molecular dynamics simulations are a powerful means of understanding conformational changes. However, it is still difficult to simulate biologically relevant time scales without the use of specialized supercomputers. Here, we introduce a goal-oriented sampling method, called fluctuation amplification of specific traits (FAST), for extending the capabilities of commodity hardware. This algorithm rapidly searches conformational space for structures with desired properties by balancing trade-offs between focused searches around promising solutions (exploitation) and trying novel solutions (exploration). FAST was inspired by the hypothesis that many physical properties have an overall gradient in conformational space, akin to the energetic gradients that are known to guide proteins to their folded states. For example, we expect that transitioning from a conformation with a small solvent-accessible surface area to one with a large surface area will require passing through a series of conformations with steadily increasing surface areas. We demonstrate that such gradients are common through retrospective analysis of existing Markov state models (MSMs). Then we design the FAST algorithm to exploit these gradients to find structures with desired properties by (1) recognizing and amplifying structural fluctuations along gradients that optimize a selected physical property whenever possible, (2) overcoming barriers that interrupt these overall gradients, and (3) rerouting to discover alternative paths when faced with insurmountable barriers. To test FAST, we compare its performance to other methods for three common types of problems: (1) identifying unexpected binding pockets, (2) discovering the preferred paths between specific structures, and (3) folding proteins. Our conservative estimate is that FAST outperforms conventional simulations and an adaptive sampling algorithm by at least an order of magnitude. Furthermore, FAST yields both the proper thermodynamics and kinetics, allowing for a direct connection with kinetic experiments that is impossible with many other advanced sampling algorithms because they provide only thermodynamic information. Therefore, we expect FAST to be of great utility for a wide range of applications.