Small Area Estimation (SAE) methods are used to obtain reliable estimates of finite population descriptive quantities of interest when domain sample sizes are too small to provide adequate precision for direct domain estimators. Standard SAE methods are based on linear mixed models (LMM) with area-specific random effects. However, due to recent advances in software and hardware capabilities, it is more and more common to have high-dimensional datasets with many potential predictors highly correlated. Therefore, dimension reduction techniques such as partial least squares (PLS) have recently gained attention to deal with these problems. Conventional PLS approaches do not allow to explicitly address the hierarchical dependence. For this reason, in this paper, we combine the standard Fay-Herriot model with a PLS technique for estimating averages at the small area level. The performance of the proposed predictors is empirically assessed in model-based simulations.