Activity landscapes (ALs) integrate structural and potency data of active compounds and provide graphical access to structure-activity relationships (SARs) contained in compound data sets. Three-dimensional (3D) ALs can be conceptualized as a two-dimensional (2D) projection of chemical space with an interpolated activity surface added as a third dimension. Such 3D ALs are particularly intuitive for SAR visualization. In this work, 3D ALs were generated on the basis of different projection methods and fingerprint descriptors, and their topologies were compared. Moreover, going beyond qualitative analysis, the use of 3D ALs for semiquantitative and quantitative potency predictions was investigated. NeuroScale, a neural network variant of multidimensional scaling, combined with Gaussian process regression (GPR) was identified as a preferred approach for generating 3D ALs that accounted for training compounds and their SAR characteristics with high accuracy. On the other hand, GPR-induced overfitting generally limited the accuracy of potency value predictions regardless of the projection method applied. However, 3D ALs enabled reliable mapping of test compounds with varying potency levels to corresponding AL regions. The most accurate mapping was achieved with NeuroScale models. Taken together, the results of our analysis indicate the high potential of 3D ALs for graphical SAR exploration and the identification of potent test compounds.