Many modern IV studies (especially Mendelian Randomization) are carried out with the two-sample design, where the samples may come from different populations. We derive a new class of linear IV estimates that are robust to sample heterogeneity. We then attempt to relax the linearity assumption and find that the two-sample design generally requires more untestable assumptions.