Biomolecular computing (BMC) aims to capture the innumerable advantages that biological molecules have gained in the course of millions of years for computational purposes. While biomolecules have resolved fundamental problems as a parallel computer system that we are just beginning to decipher, BMC still suffers from our inability to harness these properties to bring biomolecular computations to levels of reliability, efficiency and scalability that are now taken for granted with solid-state based computers. In the same way that evolutionary algorithms capture, in silico, the key properties of natural evolution, we explore an alternative approach to exploiting these properties by building virtual test tubes in electronics that would capture the best of both worlds. We describe a distributed implementation of a virtual tube, Edna, on a cluster of PCs that aims to capture the massive asynchronous parallelism of BMC. We report results from three large experiments that benchmark Edna's performance, reproduce and extend Adleman's solution to the Hamiltonian Path problem for larger families of graphs than has been possible on a single processor or has been actually carried out in wet labs, and benchmark the feasibility and performance of DNA-based associative memories. The results provide strong evidence that the paradigm of molecular computing can be implemented much more efficiently (in terms of time, cost, and probability of success) in silico than the corresponding wet experiments, at least in the range where eDNA can be practically run. Consequently, we pinpoint the appropriate range of problem sizes and properties where wet biomolecular solutions would offer superior solutions. This approach also shows an intrinsic advantages in using DNA like genomes for genetic algorithms and evolutionary computation.