In this work we study the validity of the so-called curse of dimensionality for indexing of databases for similarity search. We perform an asymptotic analysis, with a test model based on a sequence of metric spaces $(Ω_d)$ from which we pick datasets $X_d$ in an i.i.d. fashion. We call the subscript $d$ the dimension of the space $Ω_d$ (e.g. for $\mathbb{R}^d$ the dimension is just the usual one) and we allow the size of the dataset $n=n_d$ to be such that $d$ is superlogarithmic but subpolynomial in $n$. We study the asymptotic performance of pivot-based indexing schemes where the number of pivots is $o(n/d)$. We pick the relatively simple cost model of similarity search where we count each distance calculation as a single computation and disregard the rest. We demonstrate that if the spaces $Ω_d$ exhibit the (fairly common) concentration of measure phenomenon the performance of similarity search using such indexes is asymptotically linear in $n$. That is for large enough $d$ the difference between using such an index and performing a search without an index at all is negligeable. Thus we confirm the curse of dimensionality in this setting.