While the DF-NEO-MP2 method is excellent both in terms of accuracy as well as computational speed-up for small to mid-size systems it is itself still computationally prohibitive for large systems. This is due to the asymptotically quintic scaling in the integral assembly. However, this can be reduced to an asymptotically linear scaling by employing local approximations. In our implementation we employ the well established localization into electronic pair natural orbitals for the local NEO-MP2 method to reduce the prohibitive scaling of the canonical NEO-MP2 counterpart. As this prior work on Born-Oppenheimer based electronic PNO-LMP2 shows, the localization in PNOs yields in computationally very ecient approach since a minimal domain size is necessary to already recover a vast majority of the electronic correlation energy. However, this approximation was not used before on a reference wave function obtained from a multicomponent method so it has yet to be demonstrated for this case. Therefore, we employ as test system a DNA fragment build from guanine-cytosine base pairs, one of the most important molecules from biochemistry and consistently size-increasable by adding more base pairs to the strand. to our canonical DF-NEO-MP2 implementation. The results of this benchmark are shown in Fig. . By increasing the number of guaninecytosine base pairs from one to three the unaccounted correlation energy rises only from 0.23 0.40 %, so overall the recovered correlation energy is in line with previous benchmarks for the PNO method when employed in a multicomponent context. By tightening the threshold for the local density fitting and the RI approximations as well as for the domain approximations, which in Molpro is controlled by the keyword DOMOPT= {tight, very tight}, the recovered correlation energy can be increased significantly. This is also shown in Fig. exemplary for the DNA strand made by two guaninecytosine base pairs. By tightening the aforementioned thresholds, the unaccounted correlation energy decreases from 0.14 % to 0.10 %. However, by increasing the recovered correlation energy from 99.66 % to 99.86 % for the tight settings or even 99.90 % for the very tight settings, the achieved computational speed-up decreased significantly. Whereas the regular thresholds show a speed-up factor of 20, gaining 0.20 % more correlation energy via the tight settings reduces the speed-up factor to 5. With the very tight settings it is possible to recover 0.24 % more correlation energy compared to the regular thresholds but the computational speed-up reduces to a factor of 4. How- ever, it should be noted that absolute energies are of little interest, and chemical processes are described by relative energies which benefit from error compensation. For those purposes it has already been shown that the PNO based methods lead to an accuracy even below 1 kJ mol 1 . We will confirm this within the multicomponent context later with our model applications.