CG MD simulations combined with analytical models elucidate the range of temperatures and misbinding energies that allow for the successful one-pot self-assembly of distinguishable blocks into intrinsically preprogrammed structures. Accounting for the multimodal energy distribution (characteristic of DNA-based assembly) in the CG model shows that the success of programmable assembly is determined not by the random background misbinding, ε''/ε, but rather the strongest (closest to native) misbinding a sequence encounters, ε'/ε. Since ε'/ε is associated with sequences that are highly similar to the reverse complement of a chosen sequence, we were able to establish rules for generating optimal collections of sequences with low misbinding, and to estimate the maximum possible sizes of DNA-based structures that can be selfassembled with high fidelity and yield. Importantly, our design scheme can not only increase the efficiency of one-pot self-assembly, but in hierarchical assembly as well. Instead of combining, for example, two blocks per step during step-bystep assembly, 9,10 our findings suggest that one can combine more blocks into each step, provided that the development of smaller target substructures are not prone to steric hindrance problems. This would dramatically improve the low yields of current hierarchically assembled structures, and perhaps decrease the complexity involved in such assembly.