To optimize the fluorescence intensity of sensors without changing their selectivity, they i) randomly mutated (GT)15, a commonly used oligonucleotide for neurotransmitter detection, prepared ii) the respective oligonucleotides, iii) screened and iv) choose novel sequences for the next round based on the sensors with enhanced fluorescence intensities. In the first round, the library consisted of 99 mutants and led to three mutants with enhanced properties (Figure ). In the second round, the best two mutants were further mutated (library of 10 mutants) to produce an additional two mutants with enhanced brightness. Importantly, the fluorescence response to dopamine was not changed compared to the original sequence and interestingly, it could be shown that a change of only three mutations leads to a change in the nanosensors optoelectronic properties. More recently, Lambert et al. used a library of SWCNT-DNA hybrids to design sensors for aflatoxin and fumonisin. They used directed evolution approaches to further enhance the sensor response by more than three-fold. Additionally, they showed that DNA shuffling can be used to accelerate sensor optimization. For this, they (computationally) cut well performing sequences into smaller fragments, shuffled them, and used the sequence to prepare novel sensors. Compared to the introduction of single mutations, this approach allows to evolve the hybrid from one local minimum toward a new local optimum with higher performance. Using combinational shuffling, it is therefore possible to sampling larger chemical landscapes with a minimal extension of the library size. These approaches have so far a smaller throughput similar to the synthesis-limited approaches but novel assembly procedures and multiple evolution rounds can improve the numbers. . Copyright the Authors.