the agreement with the theoretical prediction is much better for the smaller solute radius. This observation is not unexpected, given that the smaller solute radius is closer to the continuum limit. What is perhaps surprising is the extent of the deviation from the theory when R$_{1}$/R$_{2}$ = 10 . For this solute radius, our simulation results deviate from the analytical prediction by about an order of magnitude. An important drawback of our MC simulation scheme is that it does not account for the details of the solvent interactions with the chucker surface. As discussed in Section I, these effects may drastically reduce the magnitude of solute-induced effects on colloid mobility. In our MC simulations, an imbalance of solute concentration across the chucker surface leads to fewer overlaps (“collisions”) on one side of the chucker than the other, and hence net motion down the concentration gradient. We expect this to correspond to Eq.(2) with a prefactor of A = (4 / 3) πR 3 $_{1}$k$_{B}$ T (simply summing collisions across the colloid surface). We would expect hydrodynamic effects at the chucker surface (if included) to decrease this prefactor: for a solvent-impermeable chucker with no slip boundary conditions, linearised hydrodynamic theory predicts that the prefactor decreases by a factor ∼ λ $^{2}$/R2 1 where λ [the lengthscale for chucker-solute interactions] is ∼ R$_{2}$ [11, 12, 28, 30]. If, on the other hand, the chucker is partially or totally permeable to solvent, the decrease in the prefactor due to these effects should be less severe [12, 27]. As discussed in Section II B, we can model the effects of solvent-chucker surface interac- tions approximately using Langevin dynamics simulations in which we vary the magnitude of the prefactor A : this corresponds to varying the hydrodynamic lengthscale λ . Figure 8 shows the effective diffusion constant D$_{eff}$ , computed as a function of k$_{c}$ using Langevin dynamics simulations with different prefactors, for R$_{1}$/R$_{2}$ = 10 . The same qualitative be- haviour is observed as in our MC simulations (compare to Figure 5): again, D$_{eff}$/D$_{1}$ increases with k$_{c}$ for small chucking rates. For large chucking rates, the effective diffusion constant de- creases in the MC simulations due to crowding effects which are not included in the Langevin Dynamics simulations For small k$_{c}$R 2 $_{2}$/D$_{1}$ , we can make a quantitative comparison between the MC and Langevin simulation results, as shown in Figure 9. Here, the Langevin simulations were run with the “naive kinetic” prefactor A = (4 / 3) πR 3 $_{1}$k$_{B}$ T . Excellent agreement is obtained between the two types of simulation. This reinforces our earlier conclusions that the MC simulations for small R$_{2}$ behave as would be predicted by hydrodynamic theory with a hydrodynamic 12